neural fuzzy logic IEEE PAPER
Evaluation of building energy consumption based on fuzzy logic and neuralnetworks applications
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Abstract. The authors have created a Neural-Fuzzy Assistant which acts as a Decision Support System and helps to perform quickly and easily the estimations of office building energy consumption. The Neural-Fuzzy Assistant presented in this paper allows the user
Development of fuzzy logic and neural network control and advanced emissions modeling for parallel hybrid vehicles
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EXECUTIVE SUMMARY This report describes the development of new control strategies and models for Hybrid Electric Vehicles (HEV) by the Ohio-State University, on a contract with the National Renewable Energy Laboratory (NREL), Golden, CO. The contract was
Fuzzy logic and neural networks
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ABSTRACT In this paper we give some basic principles of fuzzy neural computing using synaptic and somatic operations. We first briefly review the neural systems based upon conventional algebraic synaptic (confluence) and somatic (aggregation) operations. Then
A sensor-based obstacle avoidance controller for a mobile robot using fuzzy logic and neural network
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This paper proposes a sensor-based path planning method which utilizcs fuzzy logic and neural network for obstacle avoidancc of a mobile robot in uncertain environments. In order to acquire the information about the environment around the mobile robot, the ultrasonic
A hybrid neural network-fuzzy logic approach to nuclear power plant transient identification
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ABSTRACT A methodology is presented that couples pretrained artificial neural networks (ANNs) to rule-based fuzzy logic systems, for the purpose of distinguishing different transients in a Nuclear Power Plant (NPP). A model referenced approach is utilized in
Vision intelligence for precision farming using fuzzy logic optimized genetic algorithm and artificial neural network
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Summary: The purpose of the study was to develop an intelligent vision system for autonomous vehicle field operations. Fuzzy logic was used to classify the crops and weeds. A Genetic Algorithm (GA) was used to optimize and tune the fuzzy logic membership rules.
Advances in fuzzy logic, neural networks and genetic algorithms
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Keigo Watanabe 34 Recurrent FUZZYLogic Using Neural Network Emdad Khan, Fatih Unal 48 i Information Aggregating Networks based on Extended Sugeno's Fuzzy Integral Keon-Myung Lee, Hyung Lee-Kwang 56 A Neuro
Gender classification in speech recognition using fuzzy logic and neuralnetwork.
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Abstract: Nowadays classification of gender is one of the most important processes in speech processing. Usually gender classification is based on considering pitch as feature. The pitch value of female is higher than the male. In most of the recent research works
Type-2 Fuzzy Logic as a Method of Response Integration in Modular NeuralNetworks.
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ABSTRACT We describe in this paper the use of neural networks and type-2 fuzzy logic for pattern recognition. In particular, we consider the case of speaker recognition by analyzing the sound signals with the help of intelligent techniques, such as the neural networks and
Capital market applications of neural networks, fuzzy logic and genetic algorithms
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Abstract: The capital markets have numerous areas with potential applications for neural networks, fuzzy logic and genetic algorithms. Given this potential and the impetus on these technologies during the last decade, a number of studies have focused on capital market
Education Aspects: Handwriting Recognition-Neural Networks-Fuzzy Logic
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Intelligence is introduced. The originality of authors' approaches is that (i) one of the approaches to handwriting recognition (ii) elements of fuzzy logic, and (iii) neural networks, are simultaneously studied in the work. Other aim of the paper is to review such directions
Detection and estimation of defect depth in infrared thermography using artificialneural networks and fuzzy logic
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Comparison of optimized neural network with fuzzy logic for ore grade estimation
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Abstract: This paper presents a new approach to improve the performance of neural network method to estimate the grade values. The true value of ore body grade which is calculated based on the accurate data is a challenge of the mining industry. The main goal of the
Monitoring and Diagnosing Manufacturing Processes Using a Hybrid Architecture with Neural Networks and Fuzzy Logic
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Abstract The success of unattended manufacturing depends largely on control mechanisms that monitor the current machining state and take means in case of disturbances. Direct methods generally lack on-line capability, whereas indirect methods are difficult to pursue
Water Level Control by Fuzzy Logic and Neural Networks
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Abstract:The objective of this paper is to investigate and find a solution by designing the intelligent controllers for controlling water level system, such as fuzzy logic and neural network. The controllers also can be specifically run under the circumstance of system
Neural networks, qualitative fuzzy logic and granular adaptive systems
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Abstract-Though traditional neural networks and fuzzy logic are powerful universal approximators, however without some refinements, they may not, in general, be good approximators for adaptive systems. By extending fuzzy sets to qualitative fuzzy sets, fuzzy
Combining neural networks and fuzzy logic for applications in character recognition
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Abstract This thesis investigates the benefits of combining neural networks and fuzzy logic into neuro-fuzzy systems, especially for applications in character recognition tasks. The research reported in this thesis is divided into two parts. In the first part, two main
Estimation of hydraulic pressure in water networks using artificial neuralnetworks and fuzzy logic
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Abstract Hydraulic pressure is one of the most significant parameters in optimizing water distribution networks. Its simulation and estimation are essential tools in water distribution network management due to the significant effects it has on certain parameters of these
Neural networks and fuzzy logic approximation and prediction for HRV analysis
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ABSTRACT: The heart rate signal contains valuable information and its analysis has proven very useful in distinguishing healthy subject cardiograms from those of subjects with a This book Fuzzy Logic and Neural Network Handbook presents in collective form a rich set of fuzzy logic (FL) and neural network (NN) architectures, algorithms and applications, focusing on the major and latest results. All chapters were prepared by leading
pH Neutralization in CSTR using model reference neural network and fuzzy logic adaptive controlling schemes
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ABSTRACT Control of the pH neutralization process plays an important role in many applications. Model reference adaptive controller base controlling is applied for pH process and intelligent controller such as fuzzy and neural network is added along with this
An application of fuzzy logic and neural network to fingerprint recognition
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Abstract:Because fingerprint patterns are fuzzy in nature and ridge endings are changed easily by scares, we try to only use ridge bifurcation as fingerprints minutiae and also design a fuzzy feature image" encoder by using cone membership function to represent the
Weld residual stress prediction using artificial neural network and Fuzzy logicmodeling
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Artificial intelligent tools such as expert systems, artificial neural network and fuzzy logic support decision-making are being used in intelligent manufacturing systems. Success of intelligent manufacturing systems depends on effective and efficient utilization of
Introduction to Neural Networks, Fuzzy Logic and Genetic Algorithms
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This textbook explains Neural Networks, Fuzzy Logic and Genetic Algorithms from a unified engineering perspective. It combines the three techniques to minimize their weaknesses and enhance their strengths. The authors have discussed the fundamental ideas and practical methods of
Implementation and comparative study of three phase induction motor control using PID Controller, fuzzy logic and neural network techniques
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Abstract-This paper present the implementation and comparative study of dynamic behaviour of three phase induction motor with different control strategies such as PID controller, Fuzzy Logic and Neural Network techniques. The system consist of three phase
An associate design of fuzzy logic with grey-neural prediction in PID controller
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A hybrid Proportional-Integral-Derivative (PID) controller which combines the Ziegler- Nichols PID controller with the Grey-neural prediction model is presented. Different characteristics of the employed controllers have been appropriately acquired. It is easy to
Simulation of Unified Power Quality Conditioner for Power Quality Improvement Using Fuzzy Logic and Neural Networks
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Abstract One of the major concerns in electricity industry today is power quality. It becomes especially important with the introduction of advanced and complicated devices, whose performance is very sensitive to the quality of power supply. The electronic devices are
Training artificial neural networks for fuzzy logic
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Abstract. Problems requiring inferencing with Boolean logic have been implemented in perceptrons or feedforward networks, and some attempts have been made to implement fuzzy logic based inferencing in similar networks. In this paper, we present productive
Neural Network and Fuzzy Logic Approach for Satellite Image Classification: A Review
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Abstract Image classification is the important part of remote sensing, image analysis and pattern recognition. Digital Image Classification is the process of sorting all the pixels in an image into a finite number of individual classes. Landuse/Landcover classification of
An optimal design of fuzzy logic neural network using linear adapted genetic algorithm
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Abstract: Many industrial engineering design problems result in complex optimization tasks, which are difficult to solve using conventional optimization techniques. The proposed paper deals with special global optimization method widely applicable in scientific problems.
Fuzzy Logic and Genetic Algorithm for Optimizing the Approximate Match of Rules based on Backpropagation Neural Networks.
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ABSTRACT This paper presents an application of Fuzzy Logic (FL) and Genetic Algorithm (GA) for improving the approximate match of first-order Inductive Logic Programming (ILP) rules that is based on Backpropagation Neural Networks (BNN). With the help of FL, the
Neural-networkbased self-organizing fuzzy-logic automatic voltage regulator for a synchronous generator
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Abstract-Fuzzy controllers are increasingly being accepted by engineers and scientists as a viable alternative for classical controllers. While fuzzy theory provides an inference mechanism under cognitive uncertainty, computational neural networks offer exciting
Fuzzy logic and neural network approach to short term thermal Unit Commitment
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Abstract A work on neural network and fuzzy logic based technique for solving the problem of unit commitment in any electric utility is presented in this paper. The effectiveness of economic dispatch is well understood when the objective is to schedule the committed
Neural networks. lesson 9-fuzzy logic
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Fuzzy logic emerged as a consequence of the 1965 proposal of fuzzy set theory by Lotfi Zadeh. Though fuzzy logic has been applied to many fields, from control theory to artificial intelligence, it still remains controversial among most statisticians who prefer Bayesian
Agent-based Information Discovery in the World Wide Web Using a NeuralNetwork Approach and Fuzzy Logic
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Abstract This paper presents a multi-agent system for information discovery in the World Wide Web. A neural network approach is proposed to automate the process of agent-based searching and selecting WWW applications. Results from training and testing those
Study of existing work on soft computing methodologies and fusion of neuralnetwork and fuzzy logic for estimation and approximation
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Abstract:Estimation and Approximation plays a vital role in planning for future. It is up to the people, especially the business leaders to take its due advantage. Those who understand the significance of estimation, practice it very often. When performed correctly, the act of
Direct Torque Control of Induction Motor Fed by Three Phase PWM Inverter Using Fuzzy Logic and Neural Network
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Abstract–In this paper an intelligent direct torque control (DTC) is applied for induction motor control fed by three phase PWM inverter. Two control approaches using Fuzzy DTC, and Neural Network DTC are proposed and compared. The validity of the proposed control
Weighted Overlay, Fuzzy Logic and Neural Networks for Estimating Vegetation Vulnerability within the Ecological Economical Zoning of Minas Gerais, Brazil.
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Abstract. This paper describes the research carried out within the framework of the Ecological Economical Zoning of Minas Gerais (ZEE-MG) to model vegetation vulnerability derived by a number of spatial inference methods. Methods based on weighted overlay,
Applying Fuzzy Logic and Neural Network to Rheumatism Treatment in Oriental Medicine
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In this paper, we present an application of soft computing into a decision support system RETS: Rheumatic Evaluation and Treatment System in Oriental Medicine (OM). Inputs of the system are severities of observed symptoms on patients and outputs are a diagnosis of
Development of temperature-based weather forecasting models using neuralnetworks and fuzzy logic
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Abstract In critical fields such as flight, agriculture, tourism, etc, forecasting is an important issue due to its effectiveness in human life to know what will happen for unpredictable situations and events. Weather forecasts provide critical information about future weather
Applications of Neural Networks and Fuzzy Logic to Integrated Water Management Project Report
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This report is produced as a result of a joint project of the Foundation for Applied Water Research (STOWA) and Delft Cluster research programme. IHE-Delft was responsible for
Applying Fuzzy Logic to Neural Modeling.
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Fuzzy logic is an extension of Boolean logic which allows intermediate values between True and False. As in Boolean logic, a true statement is expressed by the value 1 and a false statement by the value 0. However, unlike in probability theory, the value must not be
Comparison of Neural network and Fuzzy logic control for Nonlinear model of Two link rigid manipulator
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Abstract A model with multiple inputs and multiple outputs is considered to simulate two links rigid manipulator. Its mathematical model is obtained by using Euler's Lagrange method. A new intelligent scheme based on fixed stabilization technique is proposed in this paper for
Neural network and fuzzy logic for an improved soil moisture estimation
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ABSTRACT In the last two decades, various remote sensing techniques have been evaluated and proven to be a valuable source of information for different hydrological applications. Particularly, microwave remote sensing had been frequently used as
Comparison of fuzzy logic and neural network for modelling surface roughness in EDM
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ABSTRACT Surface roughness is the main indicator of technological performances of a component for electrical discharge machining (EDM). EDM process of manganese alloyed cold-work tool steel was modelled. In this paper we used the fuzzy logic (FL) and neural
Classification of wood plates by neural networks and Fuzzy logic
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ABSTRACT The automated visual inspection is an important task for the industrial productivity. It could be applied for quality control or for replacing manual work under dangerous or repetitive activity. The classification stage in quality control of the industrial
Aircraft landing control using fuzzy logic and neural networks
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ABSTRACT Fuzzy logic is well known and widely used today in control systems. Neural networks are also used in a wide variety of systems. Combination of these two different approaches should have many beneficial properties for solving many different problems,
Hybrid Neural Genetic and fuzzy logic approach for real-time tuning of PID Controller in AVR System
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Abstract: Optimal tuning of proportional–integral–derivative (PID) controller parameter is necessary for thematic factory operation of an automatic voltage regulator (AVR) system. This study presents a novel combined genetic algorithm (GA), radial-basis function
ECG Signals Analysis using PCA with Neural Network and Fuzzy Logic
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Abstract:In the paper, a method is proposed for the analysis of the ElectroCardioGraphy (ECG) signals. ECG signals are exploited to measure the heart activity of the person. So, for the proper analysis of the ECG, a knowledge of its peaks should be there. Hence, the
Performance of mulitple linear regression and nonlinear neural networks andfuzzy logic techniques in modelling house prices
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ABSTRACT House price prediction continues to be important for government agencies insurance companies and real estate industry. This study investigates the performance of house sales price models based on linear and non-linear approaches to study the effects
Prediction, modeling and characterization of surface texturing by sulfuric etchant on non-toxic titanium bio-material using artificial neural networks and fuzzy logic
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Abstract Multilayer feed forward network, radial biased function network, generalized regression neural network and adaptive network-based fuzzy inference system (ANFIS) were used to predict the surface roughness of Ti-13Zr-13Nb alloy in etching sulfuric acid.
River ice breakup forecasting using artificial neural networks and fuzzy logicsystems
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Abstract Due to the complexity of breakup ice jam processes deterministic modelling cannot yet forecast every aspect of the timing and severity of possible consequent flooding, especially when some lead-time is needed. In most northern regions, the sparse network
Investigation of the Applicability of Neural-Fuzzy Logic Modeling for Culvert Hydrodynamics
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92 INVESTIGATION OF THE APPLICABILITY OF NEURAL-FUZZY LOGIC MODELING FOR CULVERT HYDRODYNAMICS Jonathan M. Lester and Robert N. Eli West Virginia University PO Box 6103, Morgantown, WV 26506 lesterjm@ cox. net KEY WORDS: neural-fuzzy logic,
Neural Network and Fuzzy Logic in modeling the
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Abstract-The scope of next generation manufacturing is incorporating artificial intelligence in the system. Intelligent manufacturing requires accurate prediction to achieve its goal of improved quality at lower cost. In this work the intelligent tools neural network, fuzzy logic
Electric Load Forecasting Using Neural Networks and Fuzzy Logic Systems
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Abstract In this thesis, a proposed technique for electric load forecasting is developed after introducing real time effects on the forecast. The most important factors affecting the medium term forecasting are: Temperature variations, Number of air conditioners and Number of
A Proposed Framework for Software Effort Estimation Using the Combinational Approach of Fuzzy Logic and Neural Networks
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Abstract Software effort and cost estimation has turned out to be the major challenge in IT industries. In this paper, different software effort and cost estimation techniques like algorithmic methods, expert judgment, analogy based estimation and soft computing
PERSPECTIVES FOR APPLICATION OF ARTIFICIAL NEURAL NET WORKS ANDFUZZY LOGIC SYSTEMS FOR PREDICTION AND DIAGNOSIS OF TOOL
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Status Review On Neural/Fuzzy Logic Techniques For Induction Motor Condition Monitoring
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ABSTRACT Condition monitoring of induction motors is an established maintenance strategy for the detection of incipient faults as to avoid the unexpected failure. Now a days artificial intelligence techniques are being preferred over traditional protective relays for
A Review on Prediction of ERP Outcome Measurement and User Satisfaction by Use of AI (Fuzzy Logic and Neural Networks)
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Abstract:ERP (Enterprise Resource Planning Systems) comprises of a commercial software package that promises the seamless integration of all the information flowing through the company-accounting, human resource supply chain and the consumer
Software Reliability Assessment by using Neural Networks with Fuzzy Logicbased Systems
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Abstract:Software is often a key component of the high technology systems that are so common in modern society. High reliable software is critical both to software industries and the end users of the developed software, and also for the social community generally,
Robust Self-Learning Fuzzy Logic Servo Control With Neural Network-Based Load Compensator
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Abstract:-In this paper, a fuzzy-neural control scheme composed of a sensitivity model- based self-learning fuzzy logic controller (SLFLC) and a neural network-based (NN) load estimator consisting of two off-line trained feedforward neural networks is described. The
GENDER IDENTIFICATION IN SPEECH PROCESSING USING NEURAL NETWORK AND FUZZY LOGIC
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ABSTRACT Nowadays classification of gender is one of the most important processes in speech processing. Usually gender classification is based on considering pitch as feature. The pitch value of female is higher than the male. In most of the recent research works
Rule Base Derivation for a Fuzzy Logic Controller Using Multilayered NeuralNetworks
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ABSTRACT This paper presents a rule base derivation method for fuzzy logic control of a dynamic system using neural networks without a priori knowledge of the system. Multi- layered perceptron neural networks are used to form an approximate model of the fuzzy
Student modeling using fuzzy logic and neural networks
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Abstract. In this thesis a neural network-based fuzzy modeling approach to assess student learning characteristic and update the student model in Intelligent Learning Environments is proposed. The neural network-based fuzzy diagnostic model is a general diagnostic
ARTMAP Artificial Neural Networks with Fuzzy Logic. Their Application to Study the Wave Conditions at Sines Port, Portugal
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Coastal zones are very attractive regions for human settlement. Associated to man's occupation of these areas is the attempt to minimize the natural hazards, with structures being designed to ensure quality of life and safety to neighboring populations. Dornelles (
Fuzzy logic, neural networks, expert systems and its applicative correlation models
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Based on the set of ten
Design of Fuzzy Logic Controllers with Genetic Algorithms through ArtificialNeural Networks for Controls
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ABSTRACT This paper focuses on the Genetic Algorithm learning paradigm applied to train the ANNs for balancing the cart-pole balancing system. The studied system is a control problem namely cart-pole problem. We will apply the unconventional techniques
Speed control of DC motor based on neural net and fuzzy logic
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This paper presented the speed control of DC motor based on neural net and fuzzy logic. To bypass the difficulties caused by system constraints and modelling uncertainties of the speed control of DC motor a neural network approach for on-line speed control of DC motor is We describe, in this book, recent advances on the design of intelligent systems based on fuzzy logic, neural networks, and nature-inspired optimization and their application in areas, such as intelligent control and robotics, pattern recognition, time series prediction, and
The intelligent controller based on fuzzy logic neural networks (FLNNs)
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Abstract:A hybrid network structure fuzzy logic neural networks (FLNNs) had been developed based on the theories of the Fuzzy Sets and the Articial Neural Networks. Many creative research works on the performance and the algorithm of the general learnable
A NOVEL MODEL REFERENCE INTELLIGENT ADAPTIVE CONTROL USINGNEURAL NETWORK AND FUZZY LOGIC CONTROLLER
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ABSTRACT In this paper two novel intelligent model reference adaptive controllers are proposed. In these schemes the intelligent supervisory loop is incorporated into the conventional model reference adaptive controller framework by utilizing an online growing
Neural Networks and Fuzzy Logic for improving Facies recognition from well log data
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Abstract Offshore oil exploitation is a high-risk expensive activity, potentially leading to high payoffs. Generally, many different analyses are employed to diminish the risks of perforating a dry well or letting escape a productive one. One of the analyses involves determining
Aortic Insufficiency Diagnosis tic through STFTWavelet Algorithm and Their Comparison through Fuzzy Logic and Neural Network
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Abstract:Phonocardiogram is one of the most unstable signals; therefore phonocardiogram is included of frequency and timing information. Within present study we obtained vast features through STFT and Wavelet algorithm. Our main focus is on signals analysis which
Neural network control with fuzzy logic compensation
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Abstract: This paper presents a new AI based control strategy. A dynamic neural network is used to identify the plant on-line. The control signal is then calculated iteratively according to the responses of a reference model and the identified neural model of the process. A fuzzy
Hybrid Arabic Speech Recognition System Using FFT, Fuzzy Logic and NeuralNetwork
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Abstract:computers, smartphones and many technologies nowadays are automated. Everybody is using the technology, therefore the need to make computers understand human languages (natural languages) is essential. That has made recognizing speech an
A Neural and Fuzzy Logic Controller to Improve the Performance of a Shunt Active Power Filter
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Abstract:In this paper, a neural and fuzzy logic approach is used to improve the performances of a three-phase shunt active filter where this devise principally used to compensate for current harmonics and reactive power in three-phase distribution network
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