Radial Basis Function Networks 2: New Advances in Design by Robert J. HowlettRadial Basis Function Networks 2: New Advances in Design by Robert J. Howlett

Radial Basis Function Networks 2: New Advances in Design

byRobert J. Howlett, Lakhmi C. Jain

Paperback | March 27, 2011

Pricing and Purchase Info

$237.47 online 
$274.95 list price save 13%
Earn 1,187 plum® points

Prices and offers may vary in store


In stock online

Ships free on orders over $25

Not available in stores


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 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.
Title:Radial Basis Function Networks 2: New Advances in DesignFormat:PaperbackDimensions:360 pages, 23.5 × 15.5 × 0.01 inPublished:March 27, 2011Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3790824836

ISBN - 13:9783790824834

Look for similar items by category:


Table of Contents

An overview of radial basis function networks.- Using radial basis function networks for hand gesture recognition.- Using normalized RBF networks to map hand gestures to speech.- Face recognition using RBF networks.- Classification of facial expressions with domain Gaussian RBF networks.- RBF network classification of ECGs as a potential marker for sudden cardiac death.- Biomedical applications of radial basis function networks.- 3-D visual object classification with hierarchical radial basis function networks.- Controller applications using radial basis function networks.- Model-based recurrent neural network for fault diagnosis of nonlinear dynamic systems.