Radial Basis Function Networks 1: Recent Developments in Theory and Applications by Robert J.howlettRadial Basis Function Networks 1: Recent Developments in Theory and Applications by Robert J.howlett

Radial Basis Function Networks 1: Recent Developments in Theory and Applications

byRobert J.howlett

Paperback | October 21, 2010

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The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 1 covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms, for example RBF learning using genetic algorithms. Both volumes will prove extremely useful to practitioners in the field, engineers, researchers and technically accomplished managers.
Title:Radial Basis Function Networks 1: Recent Developments in Theory and ApplicationsFormat:PaperbackDimensions:318 pages, 23.5 × 15.5 × 0.07 inPublished:October 21, 2010Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3790824828

ISBN - 13:9783790824827

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Table of Contents

Dynamic RBF networks.- A hyperrectangle-based method that creates RBF networks.- Hierarchical radial basis function networks.- RBF neural networks with orthogonal basis functions.- On noise-immune RBF networks.- Robust RBF networks.- An introduction to kernel methods.- Unsupervised learning using radial kernels.- RBF learning in a non-stationary environment: the stability-plasticity dilemma.- A new learning theory and polynomial-time autonomous learning algorithms for generating RBF networks.- Evolutionary optimization of RBF networks.