ENGINEERING RESEARCH PAPERS

neural network architecture and implementation


architecture neural network



Fast semantic extraction using a novel neural network architecture
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Abstract We describe a novel neural network architecture for the problem of semantic role labeling. Many current solutions are complicated, consist of several stages and handbuilt features, and are too slow to be applied as part of real applications that require such

Multi-layered GMDH-type neural network self-selecting optimum neural network architecture and its application to 3-dimensional medical image recognition of blood
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Abstract. In this study, 3-dimensional medical image recognition of the blood vessels in the liver is developed using a revised multilayered Group Method of Data Handling (GMDH)- type neural network self-selecting optimum neural network architecture. A revised GMDH-

A generic reconfigurable neural network architecture implemented as a network on chip
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ABSTRACT Neural Networks are widely used in pattern recognition, security applications and data manipulation. We propose a novel hardware architecture for a generic neural network, using Network on Chip (NoC) interconnect. The proposed architecture allows for

A modular neural network architecture with additional generalization abilities for high dimensional input vectors
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Page 1. A Modular Neural Network Architecture with Additional Generalization Abilities for High Dimensional Input Vectors The Proposed Modular Neural Network Architecture

A distributed discrete-time neural network architecture for pattern allocation and control
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ABSTRACT The focus of this study is how we can efficiently implement a novel neural network algorithm on distributed systems for concurrent execution. We assume a distributed system with heterogeneous computers and that the neural network is replicated on each

Seafloor classification using echo-waveforms: a method employing hybrid neural network architecture
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Abstract:This letter presents seafloor classification study results of a hybrid artificial neural network architecture known as learning vector quantization. Single beam echo-sounding backscatter waveform data from three different seafloors of the western continental shelf of

Design and analog VLSI implementation of neural network architecture for signal processing
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Abstract Biological systems process the analog signals like image and sound efficiently. To process the information the way biological systems do, we make use of Artificial Neural Networks (ANN). The focus of this paper is the implementation of the Neural Network

An integrated mixed-mode neural network architecture for megasynapse ANNs
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Abstract-This paper presents a new VLSI architecture for ANNs based on the combination of digital signalling and analog computing. It achieves a high level of parallelism as well as efficient area and power usage making very large networks possible. An implementation

Searching most efficient neural network architecture using Akaike's information criterion (AIC)
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ABSTRACT The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. Neural networks are commonly used networks in many engineering applications due to its better generalization property.

One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats
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Method: A typical P-QRS-T segment is composed of approximately 10 major turning points. Individual nodes in an AAMLP do not encode individual features, but this number can be used for order of magnitude calculations [4]. The AAMLP encodes the variance in the

A novel chaotic neural network architecture.
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The basic premise of this research is that deterministic chaos is a powerful mechanism for the storage and retrieval of information in the dynamics of artificial neural networks. Substantial evidence has been found in biological studies for the presence of chaos in the

Mixed analog-digital artificial neural network architecture with on-chip learning
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Abstract: The authors present a novel artificial-neural-network architecture with on-chip learning capability. The issue of straightforward design-flow integration of an autonomous unit is addressed with a mixed analogue-digital approach, by implementing a charge-

A neural network architecture for automated recognition of intracellular malaria parasites in stained blood films
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Abstract The global burden of malaria is enormous and the development of better laboratory diagnostic tools is a key step in malaria control recommended by the WHO. Our objective was to develop an automated tool for the recognition of intracellular malaria parasites in

Evolving neural network architecture and weights using an evolutionary algorithm
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Abstract Artificial neural networks have been applied to a variety of classification and learning tasks. The success of error correction training algorithms such as backpropagation has meant that supervised learning, where the correct outcome is known, has been the

Multi-computer neural network architecture
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A Multi-Computer NeuralNetworkArchitecture RJHowlett and SDWalters Abstract: A novel neural networkarchitecture is presented which allows the efficient execution of the back-propagation learning algorithm on a multi-computer system which has a low communications

Neural network architecture for 3D object representation
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Abstract The paper discusses a neural network architecture for 3D object modeling. A multi- layered feedforward structure having as inputs the 3D-coordinates of the object points is employed to model the object space. Cascaded with a transformation neural network

A time delay neural network architecture for efficient modeling of long temporal contexts
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Abstract Recurrent neural network architectures have been shown to efficiently model long term temporal dependencies between acoustic events. However the training time of recurrent networks is higher than feedforward networks due to the sequential nature of the

Neural Network Architecture for Crossmodal Activation and Perceptual Sequences.
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Abstract A self-organizing neural network is described that can associate between different modalities and also has the ability to learn perceptual sequences. This architecture is a step towards the development of a complete agent containing simplified versions of all major

Parallel face recognition processing using neocognitron neural network and GPU with CUDA high performance architecture
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This chapter presents an implementation of the Neocognitron Neural Network, using a high performance computing architecture based on GPU (Graphics Processing Unit). Neocognitron is an artificial neural network, proposed by Fukushima and collaborators,

Trainable Functional Link Neural Network Architecture
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ABSTRACT: The difficulty in some pattern classifications and slowness of error back- propagation algorithm cause serious problems during training such as very long training period or not converging into the desired output space. In this paper with the trainable

Evolutionary Fuzzy System for Architecture Control in Constructive Neural Network
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Abstract-This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi-Sugeno fuzzy rules and controls the architecture of a constructive neural network. The

Neural network designed on approximate reasoning architecture and its application to the pattern recognition
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1.1. Background In recent years, researches 011 neural have tliis networks (NN) and fuzzy system been accelerated. According to advancement, examinations of new fusion technology that combines the advantage of NN and fuzzy system have also become Most

GA-based Feed-forward Self-organizing Neural Network Architecture and Its Applications for Multi-variable Nonlinear Process Systems.
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Abstract In this paper, we introduce the architecture of Genetic Algorithm (GA) based Feed- forward Polynomial Neural Networks (PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in

A neural network architecture for autonomous learning, recognition, and prediction in a nonstationary world
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In a constantly changing world, humans are adapted to alternate routinely between attending to familiar objects and testing hypotheses about novel ones. We can rapidly learn to recognize and name novel objects without unselectively disrupting our memories of

Neural Network Applications in Ship Research with Emphasis on the Identification of Roll Damping Coefficient of a Ship Final Report Post Doctoral research at
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Artificial neural networks (ANNs) have found successful applications in almost every field of engineering, science and technology. The last two decades have witnessed significant theoretical developments and applications of ANNs. In the year 2006, more than 20,000

Modular Neural Network Architecture for Detection of Operational Problems in Urban Arterials
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In recent years, transportation research has revealed that problems of widespread congestion cannot be solved by building more roads or by expanding existing infrastructure. A significant part of the solution lies in better management of traffic. One of the principal

Modular Neural Network Architecture for Accurate Estimation of Dynamic System Parameters )
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Summary. Principles of employing feedforward articial neural networks for fast and robust estimation of dynamic system parameters are reviewed briey. In this approach, the neural network approximates the mapping from the system observation space to the parameter

Modified cellular neural network architecture for integrated image sensing and processing
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The paper describes a novel VLSI architecture adaptation of the cellular neural network (CNN) paradigm. It includes details of the design as well as test results of CMOS chip prototypes. 1. INTRODUCTION: CNN is a hybrid of Cellular Automata and Neural

Dynamic-node neural network architecture learning: a potential function approach
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We present a novel method for data clustering which performs classification based on a set of potential fields synthesized over the domain on input space by a number of potential function units. We propose DYPOF (DYnamic POtential Functions) neural network, which

The Architecture and Circuital Implementation Scheme of a New Cell Neural Network for Analog Signal Processing.
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Abstract: It is a difficult problem that using cellular neural network to make up of analog signal processing circuit. This paper presented the architecture of new cellular neural network SCCNN for analog signal processing circuits, designed the neural cell circuit, and

A Robust Hybrid VLSI Neural Network Architecture for Smart Optical Sensor
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Abstract This thesis introduces a novel approach to the design of circuits found in a ver-large scale integration (VLSI) implementation of an artificial neural network. A robust hybrid architecture with analog and digital elements has ken developed for a fully-parallel single-

Single-electron circuit for inhibitory spiking neural network with fault-tolerant architecture
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Abstract:An inhibitory spiking neural network that uses single-electron circuit devices is described. The network consists of a number of identical neuron circuits constructed from single-electron oscillators with coupling capacitors. The neurons are cross-connected in a

The Automation of the Classification of Economic Activities from Free Text Descriptions using an Array Architecture of Probabilistic Neural Network
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Abstract:The automation of the categorization of economic activities from business descriptions in free text format is a huge challenge for the Brazilian governmental administration in the present day. So far, this task has been carried out by humans, not all

THE APPLICABILITY OF 'OCCAM'S RAZOR'TO NEURAL NETWORK ARCHITECTURE
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ABSTRACT This paper describes a set of experiments, which are intended to show if Occam Razor applies to the architecture of neural networks. The networks that were experimented with were; single layer networks, multilayer networks and radial basis

Neural Networks and Child Language Development: Towards a 'Conglomerate'Neural Network Simulation Architecture
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Abstract Neural networks provide a basis for studying child language development in that such networks emphasise learning. We report a simulation of some key aspects of child language development during infancy. We argue that in order to simulate the uniquely

Fully interconnected Neural Network by using electronic and optoelectronic architecture
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Abstract In this paper we present a novel hardware architecture of a neural network based on optoelectronic devices and electronic techniques. The main characteristics of the architecture are that is fully programmable interconnected, avoid optic alignment problems

Mixed Analog-Digital Image Processing Cicuit Based on Hamming Artificial Neural Network Architecture
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ABSTRACT A versatile integrated circuit based on the Hamming artificial neural network (ANN) architecture is presented. The circuit operation relies on capacitive processing of sum- of-products terms, complemented with digital postprocessing allowing various complex

Neural Network for nanoscale architecture
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Abstract:In this paper, we investigate the use of analog neural networks to implement small logic function like Look Up Tables (LUT) in FPGA circuits (Field Programmable Gate Arrays). For that purpose, we present an architecture using tunable resistors or, alternatively, an

A survey of the SpiNNaker Project: A massively parallel spiking Neural Network Architecture
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ABSTRACT Scientists have through the decades tried many different approaches to creating true Artificial Intelligence (AI). There are two main goals to this endeavour; First, to create more complex and intelligent machines and software, and second, to further the

Artificial Neural Network Architecture for Solving the Double Dummy Bridge Problem in Contract Bridge
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Abstract: Card games are interesting for many reasons besides their connection with gambling. Bridge is being a game of imperfect information, it is a well defined, decision making game. The estimation of the number of tricks to be taken by one pair of bridge

Artificial Neural Network Architecture for Solving the Double Dummy Bridge Problem in Contract Bridge
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Abstract: Card games are interesting for many reasons besides their connection with gambling. Bridge is being a game of imperfect information, it is a well defined, decision making game. The estimation of the number of tricks to be taken by one pair of bridge

Middle-long term load forecasting based on dynamic architecture for artificial neural network
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Abstract Accurate middle-long term load forecasting is the most important task of the power system investment distribution, electricity load planning and management strategies. The intelligence load forecasting methods such as artificial neural networks (ANN) and support

Application of magnification control for the neural gas network in a sensorimotorarchitecture for robot navigation
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Abstract In the present article we consider the influence of the control of the magnification in a Neural Gas Network (NG). The NG is embedded in a sensorimotor architecture of a robot navigation system and serves as a classifier for the sensory system states. This

Robotic grasping: A generic neural network architecture
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Reproducing human dexterity and flexibility in unknown environments is one of the major challenges of robotics. Among the issues related to the use of robots with artificial hands of varying complexity, the definition of their kinematical configuration when grasping an

Linear array architecture implementing the backpropagation neural network
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ABSTRACT A linear digital array architecture implementing the back-propagation neural network is proposed. It is characterized by true local connections, full expansibility and congurability in terms of number and width of layers. Moreover, it allows to pipeline the

A Neural Network Modular Architecture for Network Traffic Management
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ABSTRACT With the rapid evolution of telecommunication networks, real-time traffic management is becoming more and more crucial. We propose here a neural network modular architecture for performing diagnosis at different levels of the telephone network.

Neural Network based Database Tuning Architecture
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Abstract:Database tuning is one of the most challenging tasks of a Database Administrator (DBA). DBA has to keep track of several performance indicators on a regular basis and fine tune the key system parameters for enhanced system performance. The DBA also has to

Neural network performance versus network architecture for a quick stop training application
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The relationship between neural network (NN) performance and the parameters that form the architecture of the neural network is complicated. This paper studies an issue that is still controversial-the effect of the number of neurons and number of hidden layers on NN

An Evolutionary Neural Network Architecture Optimization Algorithm for Handwritten Digits Recognition
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ABSTRACT Artificial Neural Network model is inspired by the functioning of the human brain. It is configured for a specific application, such as pattern recognition or data classification, through a learning process. In this paper we present a novel neural network

A hybrid dynamic time warping-deep neural network architecture for unsupervised acoustic modeling
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Abstract We report on an architecture for the unsupervised discovery of talker-invariant subword embeddings. It is made out of two components: a dynamic-time warping based spoken term discovery (STD) system and a Siamese deep neural network (DNN). The

An Action-tuned Neural Network Architecture for Hand Pose Estimation.
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Abstract: There is a growing interest in developing computational models of grasping action recognition. This interest is increasingly motivated by a wide range of applications in robotics, neuroscience, HCI, motion capture and other research areas. In many cases, a

NEURAL NETWORK ARCHITECTURE MOTIVATED BY INTEGRATE AND-FIRE NEURON MODEL
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Abstract: In this paper, a learning algorithm for a novel neural network architec tnre motivated by Integrate and Fire Neuron Model (IFN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It

Simple Pyramid RAM-Based Neural Network Architecture for Localization of Swarm Robots
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Abstract The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localitargets in an

VLSI implementable associative memory based on neural network architecture
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Neural network associative memories based on Hopfield's design have two failure modes. 1) Certain memories are not recallable as they do not lie in a local minimum of the system's Liapunov (energy) function, and 2) the associative memory converges to a non-memory

Using Multi-layered Feed-forward Neural Network (MLFNN) Architecture as Bidirectional Associative Memory (BAM) for Function Approximation
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Abstract: Function approximation is to find the underlying relationship from à given finite input-output data. It has numerous applications such as prediction, pattern recognition, data mining and classification etc. Multilayered feed-forward neural networks (MLFNNs) with

Neural network hardware architecture for pattern recognition in the HESS2 project.
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Abstract. In this paper, we consider the problem of implementation of neural network in the context of the level 2 trigger of HESS2 project. We propose a hardware architecture which

A Massively Parallel Architecture for Hopfield-type Neural Network Computers
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ABSTRACT This paper describes the preliminary design of a massively parallel architecture addressing the execution of Hopfield neural networks applications. The architecture, based on a SIMD approach and custom FPGA components, makes use of a fast and highly

Neural Network Control Chart Architecture for Monitoring Non-Conformities in a Poisson Process
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Abstract The uses of Neural Network (NN) models have recently been recommended as statistical quality control (SQC) tools. The advantages of NNs, particularly the robustness of the nonlinear modeling abilities, are appealing to quality control practitioners for use in

Feedback GMDH-Type Neural Network Self-Selecting Optimum Neural Network Architecture and Its Application to 3-Dimensional Medical Image Recognition of
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Abstract. The feedback Group Method of Data Handling (GMDH)-type neural network algorithm is proposed and is applied to 3-dimensional medical image recognition of the lungs, the pulmonary vessels and the bronchial trees. In this feedback GMDH-type neural

Object Recognition using Compensatory Fuzzy Min-Max Neural Network Architecture
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Abstract: Object recognition system (ORS) is divided into two parts namely, feature extraction and classification. Feature Extraction part consists rotation, translation and scale (RTS) invariant features. These features are used to train fuzzy min-max neural network with

Feedback GMDH-Type Neural Network Self-Selecting Optimum Neural Network Architecture and Its Application to 3-Dimensional Medical Image Recognition of
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Abstract. The feedback Group Method of Data Handling (GMDH)-type neural network algorithm is proposed and is applied to 3-dimensional medical image recognition of the lungs, the pulmonary vessels and the bronchial trees. In this feedback GMDH-type neural

Object Recognition using Compensatory Fuzzy Min-Max Neural Network Architecture
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Abstract: Object recognition system (ORS) is divided into two parts namely, feature extraction and classification. Feature Extraction part consists rotation, translation and scale (RTS) invariant features. These features are used to train fuzzy min-max neural network with

A structural genetic algorithm to optimize High order neural network architecture
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Abstract: A structural genetic algorithm is proposed to optimize the High Order Neural Network (HONN) architecture and the parameters of activation function. This work partitions the genes of chromosomes into control genes and parameter genes in a hierarchical form.

Effects Of Real-time Synaptic Plasticity Using Spiking Neural Network Architecture
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Abstract:Artificial Neural Networks is a promising approach to study human brain computation in hopes of achieving similar learning by artificial agents. Recent architecture design of a low-power supercomputer by the University of Manchester, the SpiNNaker,

Analog VLSI Implementation of Neural Network Architecture
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Abstract: Artificial intelligence is realized using artificial neurons. In the proposed design, we are using Artificial neural network to demonstrate the way in which the biological system processes in analog domain. The analog components like Gilbert Cell Multiplier (GCM),

10 A Three-Dimensional Neural Network Architecture
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The idea behind the presented architecture was to create a pattern recognition system using neural components. The brain was taken as a model, and although little is known about how pattern recognition is accomplished, much more is known about the cells that comprise

ARTMAP: A SELF-ORGANIZING NEURAL NETWORK ARCHITECTURE FOR FAST SUPERVISED LEARNING AND PATTERN RECOGNITION
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Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP (system learns orders of magnitude more quickly, efciently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half

FPGA DESIGN OF A MLP ARTIFICIAL NEURAL NETWORK ARCHITECTURE
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ABSTRACT This work presents the challenges and proposed solutions on implementing a FPGA based architecture of a Multilayer Perceptron (MLP) Artificial Neural Network (ANN). Choices like switching between to use either float point arithmetic or fixed point are

Adjusting MLP Neural Network Architecture through PSO Algorithm for ECG Signal Prediction
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MLP neural networks in terms of ECG signal prediction. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited

MISSED DATA FORECASTING USING FAST NEURAL NETWORK ARCHITECTURE
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ABSTRACT Wavelet function based feed forward neural network architecture is proposed for forecasting of missed data. A set of wavelet functions offers a multi-resolution approximation in signal analysis and provides localization in spatial domain. Wavelet neural network (

Implementation of Artificial Neural Network Architecture for Image Compression Using CSD Multiplier
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Abstract. This paper presents the implementation of Artificial Neural Network (ANN) architectures on FPGA for image compression and decompression. ANN's are used in wide range of applications in different domains because of their advantages over conventional

A multi-computer neural network architecture in a virtual sensor system application
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Abstract The multi-layer perceptron (MLP) neural network is recognised to have a convergence rate which is slower than desired. Multi-computer systems are attractive platforms for the implementation of neural networks, offering the potential for achieving

MODULAR OF WEIGHTLESS NEURAL NETWORK ARCHITECTURE FOR MOBILE ROBOT
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ABSTRACT Inaccurate sensors, world unpredictability and imperfect control often cause the failure of traditional navigational methods for real time mobile robots. In face of these problems adaptive systems are imperative. Neural networks have been extensively used

Neural Network Architecture for Recognition of Cursive Handwriting
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Abstract: Handwriting recognition has been a problem that computers are not efficient at. This is obviously due to the varying letter styles that exist. Today, efficient handwriting recognition is limited to ones, using hardware like light pens wherein the strokes are

Analog VLSI Implementation of Novel Hybrid Neural Network Multiplier Architecture
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Abstract Neural networks are suitable to resolve problems where conventional resolution methods fail. The multipliers form a basic and important block in realising a neural network and this is commonly known as Synapse. Their roles are to multiply an input current with

A SCALABLE DIGITAL ARCHITECTURE OF A KOHONEN NEURAL NETWORK
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ABSTRACT Kohonen self-organizing feature maps are unsupervised learning neural networks that categorize or classify data. Efficient hardware implementation of such neural networks requires the definition of a certain number of simplifications to the original 1. INTRODUCTION In the paper, a method for synthesizing a neural network (NN) with a heterogeneous architecture designed for the approximation of continuous functions of several variables is suggested. The architecture of a neural network is meant to be a set of

Impacts of Neural Network Architecture upon Image Classification: A Preliminary Study
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Abstract. The paper reports part of our research efforts with the aim to investigate whether neural network architectures can affect the performance of image classification. Here we initially consider multi-layer perceptron (MLP) neural networks and adaptive-resonance-

An Artificial Neural Network Approach for Pulse Classification in Electro Chemical Discharge Machining (ECDM)–Designing of Neural Network Architecture
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Abstract This paper presents the pulse classification of the ECDM process using artificial neural networks (ANN). An Electro Discharge Machining (EDM) machine was modified by incorporating an electrolyte system and by modifying the control system. Gap voltage and

AUTOMATIC FEATURE SELECTION AND ARCHITECTURE OPTIMIZATION FOR NEURAL NETWORK BASED WIND POWER FORECASTING
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Summary We demonstrate the applicability of meta-learning techniques for improving a neural network based short term wind forecast system. Automatic Feature Selection (AFS) and network architecture optimization are now possible due to the boost in computation

An Artificial Neural Network Approach for Pulse Classification in Electro Chemical Discharge Machining (ECDM)–Designing of Neural Network Architecture
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Abstract This paper presents the pulse classification of the ECDM process using artificial neural networks (ANN). An Electro Discharge Machining (EDM) machine was modified by incorporating an electrolyte system and by modifying the control system. Gap voltage and

AUTOMATIC FEATURE SELECTION AND ARCHITECTURE OPTIMIZATION FOR NEURAL NETWORK BASED WIND POWER FORECASTING
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Summary We demonstrate the applicability of meta-learning techniques for improving a neural network based short term wind forecast system. Automatic Feature Selection (AFS) and network architecture optimization are now possible due to the boost in computation

DIGITAL ARCHITECTURE TOR THE EMULATION OF A BIOLOGY-ORIENTED NEURAL NETWORK
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A new digital ASIC architecture for the emulation of a dynamic neural network model is presented. The biology oriented model of the University of Marburg [1] is used. The chip architecture offers full cascadability and on-chip parameter storage. It allows a dense and

Hand Written Digit Recognition Using Elman Neural Network on Master-Slave Architecture
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Abstract--Objective of this work is to recognize the hand written digits represented in black- and-white rectangular pixel displays using Elman neural network (ENN). ENN is one of the simplest supervised multi layer neural networks. We train the network with parallel

Neural Network Architecture for Self-Tuning Manipulator Control
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Abstract Recently, there have been a great deal of interests in nonlinear control using neural networks due to their nonlinear mapping and trainability characteristics. Among these, the control paradigm combining traditional adaptive control and neural networks appear to

DESIGN AND IMPLEMENTATION OF ADAPTIVE TWO-DIMENSIONAL MULTILAYER NEURAL NETWORK ARCHITECTURE FOR IMAGE COMPRESSION
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This chapter discusses design, modeling and simulation of the adaptive two-dimensional multilayer neural network architecture proposed for image compression and decompression. This chapter also discusses implementation results and analysis of the proposed

Neural Network-Based Hardware Architecture multi-sensor for Monitoring Forest fires in Wireless Sensors Network
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Abstract:In this paper, we present and evaluate multi-sensor hardware architecture for monitoring forest fires using wireless sensors network (WSN). In the proposed architecture, each node is equipped with various sensors for temperature and carbon monoxide in

An inverse model of magnetorheological dampers with optimal neural network architecture
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Abstract:-This paper presents an emulation of the inverse dynamics of magnetorheological dampers, using a dynamic model proposed [1], which is modified to describe the actual behavior of the device, also a perceptron neural network is constructed and trained under

Implementation of RBF Neural Network Reconfigurable Architecture–A NoC Design Strategy
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Abstract The aim of the project is to design an Artificial neural network by using Network on Chip (NoC). Artificial Neural Networks (ANNs) are widely used in various applications such as recognition, security, Computer learning and so on. To meet requirements of higher

Artificial Neural Network Architecture Design by Increased Evolutionary Learning
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Abstract- This paper improves the role of adaptive nature of new Evolutionary Algorithm (EA) [19] in designing Artificial Neural Network (ANN) using the proper selection mechanism. The proposed EA has been used for two purposes. One is generalization of architecture. In

DESIGN AND VLSI IMPLEMENTATION OF MULTILAYERED NEURAL NETWORK ARCHITECTUREUSING PARALLEL PROCESSING AND PIPELINING
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ABSTRACT In this paper, an optimized high speed parallel processing architecture with pipelining for multilayer neural network for image compression and decompression is implemented on FPGA (Field-Programmable Gate Array). The multilayered feed forward

Diagnosing Angina Using a Simple Neural Network Architecture
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ABSTRACT: The aim of the study was to research the use of a simple neural network in diagnosing angina in patients complaining of chest pain. A total of 887 records were extracted from the electronic medical record system (EMR) in Selayang Hospital, Malaysia

A neural network architecture for learning temporal information
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Abstract In this paper, we propose a neural network architecture that is capable of the continuous learning of multiple, possibly overlapping, arbitrary input sequences relatively quickly, autonomously and online. The architecture has been constructed according to

OPTIMIZATION OF NEURAL NETWORK ARCHITECTURE FOR THE APPLICATION OF DRIVER FATIGUE MONITORING SYSTEM
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ABSTRACT Apart from heart disease and stroke, road crashes are identified as the top killer in Malaysia, claiming an average of 19 deaths per day in 2014. The majority of these traffic fatalities are attributed to the human errors, such as fatigue driving. Current law

Implementation of artificial neural network in optical computing architecture for pattern identification
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ABSTRACT The paper indicates the advantages offered by the optical techniques for implementation of neural network model for pattern recognition tasks. A linear associative memory model is presented which can identify a pattern from stored patterns through the

Fully-Digital, Highly-Modular Architecture of a Hamming-like Neural Network.
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A practical fully-digital network for pattern recognition is presented here. It consists basically of a Hamming-like Network architecture permitting modularity and is suitable for both semi- custom VLSI and FPGA implemantations A Hamming Neural Network (HNN) is a

Neural Network Designed On Fuzzy Interface Architecture
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Abstract. A set of neural networks designed on fuzzy interface architecture, which employed the fuzzy interface system in improving the defects of the conventional neural networks, are proposed. By using the error backpropagation learning procedure, the proposed networks

Neural Network Based Routing Algorithm for Cognitive Packet Network Architecture
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Abstract This journal focuses on introducing an adaptive routing algorithm to be used on Wide Area Network (WAN) like Internet, based on the Cognitive Packet Network (CPN) architecture to enhance the Quality of Service (QoS) delivered to the end users. This

Bayesian Neural Network with Recurrent Architecture for Time Series Prediction
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Abstract: In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time

OBJECT ORIENTED PROGRAMMING ARCHITECTURE FOR NEURAL NETWORK MODULES ABSTRACT ARCHITECTURE FOR ROBOT SOFTWARE
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An abstracted design of intelligent software constructed of NN modules is investigated. The root of the design is in general three-tire architecture that separates presentation from data and processing model as in MVC [4]. Having NN module designed at hand, the question

neural networks using back propagation algorithm



Genetic algorithm based back propagation neural network performs better than back propagation neural network in stock rates prediction
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Abstract The prevailing notion in society is that wealth brings comfort and luxury, so it is a challenging and daunting task to find out which is more effective and accurate method for stock rate prediction so that a buy or sell signal can be generated for given stocks. This

Improving the performance of backpropagation neural network algorithm for image compression/decompression system
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Abstract: Problem statement: The problem inherent to any digital image is the large amount of bandwidth required for transmission or storage. This has driven the research area of image compression to develop algorithms that compress images to lower data rates with

ATHENA: A knowledge-based hybrid back propagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci
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Abstract Background: Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex

Learning perceptron neural network with backpropagation algorithm
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Abstract. In this paper, the multilayer perceptron neural network is described and the architecture, performances and the possibilities of using it to solve certain concrete problems are analyzed. At the same time, the backpropagation algorithm of learning multilayer

Principles of training multi-layer neural network using backpropagation algorithm
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To teach the neural network we need training data set. The training data set consists of input signals (x1 and x2)target (desired output) z. The network training is an iterative process. In each iteration weights coefficients of nodes are modified

An Analysis of Chaotic Noise Injected to Backpropagation Algorithm in FeedforwardNeural Network
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Abstract There have been much interest in applying noise to neural networks in order to observe their effect on network performance. In our previous research, we have proposed a new modified backpropagation learning algorithm, in which chaotic noise is added into

Backpropagation neural network algorithm for forecasting soil temperatures considering many aspects: a comparison of different approaches
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Abstract--Artificial Neural Networks (ANNs) are interconnected collections of processing units which have been used in different applications. The objective of this paper is to develop an ANN model to estimate soil temperature for any day by using various previous

A Hilbert serial overrelaxation backpropagation algorithm for training Hilbert neural network
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Abstract The theory of neural networks on a non-Euclidean space is a new research topic and has drawn many people's attention in recent years. In this paper, a M layers feed- forward neural network acting on a Hilbert space is constructed and a corresponding

Superresolution of multi-frequency signals using multilayer neural network supervised bybackpropagation algorithm
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ABSTRACT Multi-frequency signal classification is discussed using multilayer neural networks supervised by the backpropagation algorithm. Several novel properties of the neural network are provided. First. the neural network can detect the frequencies. which

A Novel Fast Fuzzy Neural Network Backpropagation Algorithm for Colon Cancer Cell Image Discrimination
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Abstract. In this paper a novel fast fuzzy backpropagation algorithm for classification of colon cell images is proposed. The experimental results show that the accuracy of the method is very high. The algorithm is evaluated using 116 cancer suspects and 88 normal colon

Position Control of Manipulator's Links Using Artificial Neural Network withBackpropagation Training Algorithm
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Abstract:This paper describes about application of artificial neural network for controlling the position of manipulator's links. In this case, the manipulator has three degree of freedoms and the manipulator is implemented for drilling a printed circuit board. In this

BETTER PERFORMANCE OF ORTHONORMAL-BACKPROPAGATION NEURAL NETWORK ALGORITHM IN IDENTIFICATION OF OBJECT FEATURES IN
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Abstract-Any 3D object can be registered in stereoscopic camera as 2D image pair similar to that of the left eye view and to that of right eye view seen by human eyes. From the stereo pair of images displayed on a screen, the viewer could achieve 3D perceptron by wearing

A Neural Network Based Approach To Network Intrusion Detection And Analyzing Different Backpropagation Algorithm Training Approaches
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ABSTRACT To detect unwanted intrusions with more efficiency soft computing techniques are widely used.target is quiet elusive to provide much secured information system. In this paper artificial neural network is used for network intrusion detection. Different

Neural Network Model of the Backpropagation Algorithm
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Abstract We apply a neural network to model neural network learning algorithm itself. The process of weights updating in neural network is observed and stored into file. Later, this data is used to train another network, which then will be able to train neural networks by

Recurrent Neural Network with Backpropagation Through Time Learning Algorithm for Arabic Phoneme Recognition
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Abstract: The study on speech recognition and understanding has been done for many years. In this paper, we propose a new type of recurrent neural network architecture for speech recognition, in which each output unit is connected to itself and is also fully

Improved neural network backpropagation with genetic algorithm based parameter tuning for classification problem
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Page 1. IMPROVED NEURALNETWORKBACKPROPAGATION WITH GENETIC ALGORITHM BASED PARAMETER TUNING FOR CLASSIFICATION PROBLEM Chapter 2 reviews on neural network, backpropagationalgorithm, improved error function and activation function.

OMBP: Optic Modified BackPropagation training algorithm for fast convergence of Feedforward Neural Network
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Abstract: In this paper, we propose an algorithm for a fast training and accurate prediction for Feedforward Neural Network (FNN). In this algorithm OMBP, we combine Optic Backpropagation (OBP) with the Modified Backpropagation algorithm (MBP). The weights

Prediction of plasma etching using genetic-algorithm controlled backpropagation neural network
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Abstract: A new technique is presented to construct a predictive model of plasma etch process. This was accomplished by combining a backpropagation neural network (BPNN) and a genetic algorithm (GA). The predictive model constructed in this way is referred to

A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicking
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Abstract Recent years have seen a surge of interest in multilayer neural networks fueled by their successful applications in numerous image processing and computer vision tasks. In this article, we describe a fast C++ implementation to train a multilayer neural network with Artificial neural network implementation on a single FPGA of a pipelined on-line backpropagation

FREE DOWNLOAD . by using large Xilinx devices with embedded memories alongside the projection used in the systolic architecture. These physical and architectural features – together with the combination of FPGA reconfiguration properties with a design flow based on generic VHDL – create an

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