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Radial Basis Function (RBF) Neural Network Control for Mechanical Systems

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to;

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Algorithms and Architectures

structures for achieving effective systems, and illustrates them with examples. This volume includes Radial Basis Function networks, the;

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Neuro-Fuzzy Equalizers for Mobile Cellular Channels

(type-2 FAF), compensatory neuro-fuzzy filter (CNFF), and radial basis function (RBF) neural network. Neuro-Fuzzy Equalizers for;

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Neuro-Fuzzy Equalizers for Mobile Cellular Channels

(type-2 FAF), compensatory neuro-fuzzy filter (CNFF), and radial basis function (RBF) neural network. Neuro-Fuzzy Equalizers for;

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On Credit Scoring Estimation

bank. Our results indicate that the methods used, namely the logistic regression, multi-layer perceptron (MLP) and radial basis function (RBF;

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Wireless Communication System Based Coded Ofdm Transceiver

importance lies in using the radial basis function (RBF) network equalizer as a classification problem, where the idea is to map the received complex;

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Prediction of Software Reliability Using Neural Network & Fuzzy Logic

thesis, we explore an alternative approach using two types of neural networks (NN) models, the feedforward and the Radial basis function. We also;

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Neural Networks For Pattern Recognition

, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates;

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Neural Networks

the Boltzmann machine and Radial-Basis function networks, and self-organising networks such as Kohonen's self-organizing maps. Other networks;

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Pattern Recognition Using Neural Networks

functional link, radial basis function, learning vector quantanization and self-organizing. The author also discusses recent clustering methods. This;

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Regularized Radial Basis Function Networks

Simon Haykin is a well-known author of books on neural networks. An authoritative book dealing with cutting edge technology. This book has;

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Neural Network Control of Nonlinear Discrete-Time Systems

framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete;

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Neural Networks Modeling and Control

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and;

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Adaptive Wireless Transceivers

Radial Basis Function (RBF) assisted equalisers embedded into adaptive modems supported by turbo channel coding and turbo channel equalisation;

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Kinematic Control of Redundant Robot Arms Using Neural Networks

. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural;

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Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems

networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief;

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Building Neural Networks

This practical introduction describes the kinds of real-world problems neural network technology can solve. Surveying a range of neural;

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Neural Networks and Statistical Learning

radial basis function network; * recurrent neural networks; * nonnegative matrix factorization; * independent component analysis;

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Adaptive Sliding Mode Neural Network Control for Nonlinear Systems

Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control;

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Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms

basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning. Neural networks display genuine promise;

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Robust Adaptive Control for Fractional-Order Systems with Disturbance and Saturation

; disturbance observer based neural control for an uncertain fractional-order rotational mechanical system; adaptive neural tracking control for;

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Neural Network Control Of Robots And Nonlinear Systems

controlling on-line so that performance improves automatically. Neural network controllers are derived for robot manipulators in a variety;

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Passive Network Synthesis

networks, which is motivated by the vibration control based on a new type of mechanical elements named inerter. It introduces important fundamental;

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Neural Network Modeling

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network;

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Multi model Jumping Systems Robust Filtering and Fault Detection

well as higher order moment and neural network-based fault detection methods for multi-model jumping systems, allowing them to grasp the;

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Nonlinear Dynamical Systems

nonlinear input-output maps The development of robust controllers and filters The capability of neural networks to approximate functions and dynamic;

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Artificial Neural Networks in Biological and Environmental Analysis

research and teaching programs, but also for the continued interest in the use of neural network tools in scientific inquiry. The book covers;

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