Radial Basis Function Networks 2 : New Advances in Design /

Radial Basis Function Networks 2 : New Advances in Design /

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Bibliographic Details
Author / Creator:Howlett, Robert J.
Imprint:Heidelberg : Physica-Verlag HD : Imprint : Physica, 2001.
Description:1 online resource (xix, 360 pages).
Language:English
Series:Studies in Fuzziness and Soft Computing, 1434-9922 ; 67
Studies in fuzziness and soft computing ; 67.
Subject:
Computer science.
Artificial intelligence.
Optical pattern recognition.
Artificial intelligence.
Computer science.
Optical pattern recognition.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11079039
Hidden Bibliographic Details
Other authors / contributors:Jain, L. C.
ISBN:9783790818260
3790818267
9783790824834
3790824836
3790818267
Summary: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.
Other form:Print version: 9783790824834
Standard no.:10.1007/978-3-7908-1826-0
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.

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