character recognition using neural network
Character Recognition using Neural Networks.
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Abstract This paper presents creating the Character Recognition System, in which Creating a Character Matrix and a corresponding Suitable Network Structure is key. In addition, knowledge of how one is Deriving the Input from a Character Matrix must first be
Handwritten character recognition using neural network architectures
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Abstract We have developed a neural-network architecture for recognizing handwritten digits. This network has 1% error rate with about 7% reject rate on handwritten zipcode digits provided by the US Postal Service. In this paper, we discuss implementing this
Handwritten character recognition using neural network
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ABSTRACT Objective is this paper is recognize the characters in a given scanned documents and study the effects of changing the Models of ANN. Today Neural Networks are mostly used for Pattern Recognition task. The paper describes the behaviors of different Models
Handwritten Tamil character recognition using neural network
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Abstract A Neural Network approach is proposed to build an automatic off-line handwritten Tamil character recognition system. We have used a Back Propagation Network (BPN) as a character recognizer. Once trained, the network has a very fast response time. However,
CHARACTER RECOGNITION USING NEURAL NETWORKS
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ABSTRACT Numerous advances have been made in developing intelligent systems, some inspired by biological networks. The paper discuses about then usefulness of neural networks, more specifically the motivations behind the development of neural networks,
Neural networks for pattern recognition
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An example character recognition 1 The term pattern recognition encompasses a wide range of information processing problems of great practical significance, from speech recognition and the classification of handwritten characters, to fault detection in machinery
Handwritten English character recognition using neural network
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Abstract Neural Networks are being used for character recognition from last many years. This paper presents creating the Character Recognition System, in which Creating a Character Matrix and a corresponding Suitable Network Structure is key. The Feed
Backpropagation applied to handwritten zip code recognition
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Artificial neural networks: A tutorial
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Well-known applications include ' character recognition, speech recognition, EEG waveform classification Modeling a biological nervous system using ANNs can also increase computer science, artificial intelligence, statistics/ mathematics, pattern recognition, computer vision
Neural network based feedback scheduler for networked control system with flexible workload
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Most control applications closed over a shared network are suffering from the time-varying characteristics of flexible network workload. This gives rise to non-deterministic availability of communication resources and may significantly impact the control performance. In the
A novel nonlinear neural network ensemble model for financial time series forecasting
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In this study, a new nonlinear neural network ensemble model is proposed for financial time series forecasting. In this model, many different neural network models are first generated. Then the principal component analysis technique is used to select the appropriate
Intelligent learning of fuzzy logic controllers via neural network and genetic algorithm
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ABSTRACT Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to
Bayesian neural network for rainfall-runoff modeling
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In this paper, a Bayesian learning approach is introduced to train a multilayer feed- forward network for daily river flow and reservoir inflow simulation in a cold region
Development of a neural network algorithm for retrieving concentrations of chlorophyll, suspended matter and yellow substance from radiance data of the ocean color
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ABSTRACT An algorithm is presented to retrieve the concentrations of chlorophyll a, suspended pariclulate matter and yellow substance from normalized water-leaving radiances of the Ocean Color and Temperature Sensor (OCTS) of the Advanced Earth Observing Satellite (
An ant colony optimization algorithm for continuous optimization: application to feed forward neural network training ABSTRACT Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging behaviour of real ant colonies. Originally, the method was introduced for the application to discrete optimization problems. Recently we proposed a first ACO variant for
swarm optimization with neural networks
Training product units in feedforward neural networks using particle swarm optimization
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ABSTRACT Product unit (PU) neural networks are powerful because of their ability to handle higher order combinations of inputs. Training of PUs by backpropagation is however difficult, because of the introduction of more local minima. This paper compares training of a
FPGA implementation of particle swarm optimization for inversion of large neural networks.
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ABSTRACT Particle swarm inversion of large neural networks is a computationally intensive process. By the implementing a modified particle swarm optimizer and neural network in reconfigurable hardware, many of the computations can be preformed simultaneously,
An evolutionary race: a comparison of genetic algorithms and particle swarm optimizationused for training neural networks
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Abstract This paper compares the performance of genetic algorithms (GA) and particle swarm optimization (PSO) when used to train artificial neural networks. The networks are used to control virtual racecars, with the aim of successfully navigating around a track in
Cellular neural networks for gray image noise cancellation based on a hybrid linear matrix inequality and particle swarm optimization approach
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Abstract This paper describes a technique for gray image noise cancellation. This method employs linear matrix inequality (LMI) and particle swarm optimization (PSO) based on cellular neural networks (CNN). We use two images that one is desired image and the
A variation of particle swarm optimization for training of artificial neural networks
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Particle swarm optimization (PSO) is a stochastic global optimization method (Eberhart Kennedy, 1995) that belongs to the family of Swarm Intelligence and Artificial Life. Similar to artificial neural networks (ANN) and genetic algorithms (GA) which are the simplified
Particle Swarm Optimization models applied to Neural Networks using the R language
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Abstract: There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get
A particle swarm optimization approach for training artificial neural networks with uncertain data
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Abstract. Artificial neural networks are powerful tools to learn functional relationships between data. They are widely used in engineering applications. Recurrent neural networks for fuzzy data have been introduced to map uncertain structural processes with
On comparison between swarm intelligence optimization and behavioral learning concepts using artificial neural networks (an over view)
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ABSTRACT Generally, in nature, non-human creatures perform adaptive behaviors to external environment they are living in. ie animals have to keep alive by improving there behavioral ability to be adaptable to there living environmental conditions. This paper
Training of fuzzy neural networks via quantum-behaved particle swarm optimization and rival penalized competitive learning.
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Abstract: There are some difficulties encountered in the application of fuzzy Radial Basis Function (RBF) neural network. One of them is how to determine the number of hidden rule neurons and another difficulty is about interpretability. In order to overcome these
Particle swarm optimization applied to parameters learning of probabilistic neural networks for classification of economic activities
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Automatic text classification and clustering are still very challenging computational problems to the information retrieval (IR) communities both in academic and industrial contexts. Currently, a great effort of work on IR, one can find in the literature, is focused on
Comparing Classification Performances between Neural Networks and Particle Swarm Optimization for Traffic sign recognition
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Abstract:-This paper compares classification performances of two techniques for traffic sign recognition, namely, neural networks and particle swarm optimization. Neural networks and particle swarm optimization are applied to the problem of identifying all types of traffic
Particle Swarm Optimization to Pre-Train Artificial Neural Networks: Selecting Initial Training Weights for Feed-Forward Back-Propagation Neural Networks
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Abstract Performance1 of supervised training of Artificial Neural Networks (ANNs) depends on several factors, including neural network architecture, number of neurons in hidden layers, the neurons' activation functions, and selection of initial network parameters (
Predict Hypotension Events during Spinal Anesthesia Based on Particle Swarm Optimization Neural Networks
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Abstract. Neural network model based on particle swarm optimization (PSO) was established for predicting hypotension during general anesthesia. The BP neural network parameters optimized by pso, and learning samples are trained and modeled by BP neural network with optimal
Modelling and Design of Microwave Structures Using Neural Networks and Particle Swarm Optimization
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Abstract:Optimization of design parameters based on electromagnetic simulation of microwave circuits is a time-consuming and iterative procedure. To provide a fast and fairly accurate frequency response for a given case-study, this paper employs a neural network
Evolving fuzzy neural networks by particle swarm optimization with fuzzy genotype values
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Abstract: Particle swarm optimization (PSO) is a well-known instance of swarm intelligence algorithms and there have been many researches on PSO. In this paper, the author proposes an extension of PSO for solving fuzzy-valued optimization problems. In the
Variants of Particle Swarm Optimization in Enhancing Artificial Neural Networks
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Abstract: Over the past decade, researchers have positively optimized and enhanced numerous applications through the use of Particle Swarm Optimization, such as solving convergence and local minima problems in Artificial Neural Networks. This is due to its
Designing Artificial Neural Networks (ANN) using Particle Swarm Optimization Algorithms
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Abstract Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function and the learning algorithm used to train the set of synaptic weights. Here, an interesting problem emerges: How to
Optimum Shape Design of Double-Layer Grids by Particle Swarm Optimization UsingNeural Networks
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Abstract In this paper, an efficient method is proposed for optimum shape design of double- layer grids. In optimization process, the weight of structure is considered as objective function. The design variables are the number of spans divisions of grid in two directions,
Cellular Neural Networks for Medical Image Noise Cancellation Based on Particle Swarm Optimization
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ABSTRACT In this paper, a novel method for designing templates of cellular neural networks (CNNs) is discussed to cancel the image noise. The discretetime cellular neural network (DTCNN) combining with particle swarm optimization (PSO) is applied to medical image
POLYNOMIAL APPROXIMATION USING PARTICLE SWARM OPTIMIZATION OF LINEAR ENHANCED NEURAL NETWORKS WITH NO HIDDEN LAYERS
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Abstract: This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic
The Impact of Particle Swarm Optimization Method on the Improvement of Bankruptcy Predictability Using Neural Networks
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Abstract One of the common approaches to predicting the bankruptcy is to use neural network models. Among neural network methods, multi-layer perceptron method is a supervised learning algorithm which is highly capable of predicting and classifying the
Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks
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Networks [63] in which they showed that every neural network can be expressed using propositional logic, and that many choices of network topologies can yield equivalent
SIMULATION AND PARAMETER OPTIMIZATION OF GMAW PROCESS USING NEURAL NETWORKS AND PARTICLE SWARM OPTIMIZATION ALGORITHM
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To improve the corrosion resistant properties of carbon steel usually cladding process is used. It is a process of depositing a thick layer of corrosion resistant material over carbon steel plate. Most of the engineering applications require high strength and corrosion
Particle swarm optimization of artificial neural networks for autonomous robots
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Abstract Artificial neural networks (ANNs), especially when they have feedback connections, are potentially able to produce complex dynamics, and therefore have received attention in control applications. Although ANNs are powerful, designing a network can be a difficult
USING HYBRID ARTIFICIAL BEE COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION FOR TRAINING FEED-FORWARD NEURAL NETWORKS.
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ABSTRACT The Artificial Bee Colony Algorithm (ABC) is a heuristic optimization method based on the foraging behavior of honey bees. It has been confirmed that this algorithm has good ability to search for the global optimum, but it suffers from the fact that the global best
Image Recognition Using Artificial Neural Networks with Particle Swarm OptimizationBased on Hardware FPGA
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Abstract: In this paper, a medical image recognition using Artificial Neural Networks (ANN) trained by Particle Swarm Optimization based on hardware implementation of Field Programmable Gate Array (FPGA) is presented, where the adaption of the Artificial Neural
Nonlinear Electrical Impedance Tomography reconstruction using Artificial Neural Networksand Particle Swarm Optimization
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Recent medical imaging technologies, such as Electrical Impedance Tomography (EIT), offer the advantages of being noninvasive and it does not generate ionizing radiation. The main difficulty in applying EIT is to solve a very ill-posed nonlinear inverse problem. Given
Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm for Skin Color Segmentation
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ABSTRACT: The applications of skin color recognition are extended in utilizations both in content based analysis and human computer interactions. Therefore, achieving a useful method for segment the skinlike pixels can help the presented problems. In this paper a
Power Production Forecasting for Photovoltaic Generation Systems via Neural Networkswith Particle Swarm Optimization Kalman Learning
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Resumen This paper focusses on applications of neural networks for forecasting in photovoltaic arrays. The Particle Swarm Optimization technique is applied for tuning the parameters of Extended Kalman Filter training algorithm for data modeling in smart grids.
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