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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 ;...

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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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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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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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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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Evolutionary Algorithms and Neural Networks

feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the;

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Statistics and Neural Networks

; Radial Basis Function networks; techniques for optimizing predictions; approaches to the analysis of latent structure, including probabalistic;

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Evolutionary Machine Learning Techniques

neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy;

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Evolutionary Machine Learning Techniques

neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy;

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Regularized System Identification

machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real;

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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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Neural Networks and Statistical Learning

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

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Fundamentals of Computational Intelligence

is both uniform and consistent in style and notation. * Discusses single-layer and multilayer neural networks, radial-basis function networks;

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Hausdorff Calculus

systems. Topics such as the Hausdorff diffusion equation, Hausdorff radial basis function, Hausdorff derivative nonlinear systems, PDE modeling;

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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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Soft Starting of Induction Motors

back propagation basedand radial basis neural networks were trained, withdata obtained using simulations, to estimatedifferent parameters;

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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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Regularized Semigroups and Non-Elliptic Differential Operators

REGULARIZED SEMIGROUPS AND NON-ELLIPTIC DIFFERENTIAL OPERATORS discusses the semi-group approach to non-elliptic differential operators;

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Cambridge Monographs on Applied and Computational Mathematics

estimate parameters, usually multidimensional, by approximation and interpolation. Radial basis functions are a powerful tool which work well;

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Spherical Radial Basis Functions, Theory and Applications

This book is the first to be devoted to the theory and applications of spherical (radial) basis functions (SBFs), which is rapidly emerging;

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Cambridge Monographs on Applied and Computational Mathematics

processing. Radial basis functions are modern, powerful tools which are being used more widely as the limitations of other methods become apparent;

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Electric Power Systems in Transition

quality of electric service. The function of state estimation is reviewed in this book and is becoming more important, because it is the primary;

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New Developments in Approximation Theory

design, a tool in engineering and medical technology, radial basis functions, bivariate spline interpolation, and subdivision algorithms.;

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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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Optimization Techniques in Computer Vision

optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained;

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Inverse Problems

The overall goal of the book is to provide access to the regularized solution of inverse problems relevant in geophysics without requiring;

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