Radial Basis Function Neural Networks with Sequential Learning, Progress in Neural Processing

This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as minimal resource allocation networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of the existing theory of RBF networks and applications is given at the beginning.

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The primary objective of this book is to present a sequential learning algorithm for a radial basis function (RBF) neural network and to evaluate its performance on problems from such areas as function approximation, pattern classification, chaotic time series prediction, nonlinear signal processing, nonlinear dynamic system identification, and communication channel equalization. The authors, after demonstrating the development of the algorithm (dubbed the Minimal Resource Allocation Network), apply it to a range of problems in the above areas and argue that it can, with the most compact network size, achieve better or similar performance than other learning algorithms. Annotation c. Book News, Inc., Portland, OR (booknews.com)