Radial Basis Function Neural Networks: Theory and Applications

  • Paweł Strumiłło
  • Władysław Kamiński
Part of the Advances in Soft Computing book series (AINSC, volume 19)


Essential theory and main applications of feed-forward connectionist structures termed radial basis function (RBF) neural networks are given. Universal approximation and Cover’s theorems are outlined that justify powerful RBF network capabilities in function approximation and data classification tasks. The methods for regularising RBF generated mappings are addressed also. Links of these networks to kernel regression methods, density estimation, and nonlinear principal component analysis are pointed out. Particular attention is put on discussing different RBF network training schemes, e.g. the constructive method incorporating orthogonalisation of RBF kernels. Numerous, successful RBF networks applications in diverse fields such as signal modelling, non-linear time series prediction, identification of dynamic systems, pattern recognition, and knowledge discovery are outlined.


Radial Basis Function Radial Basis Function Neural Network Radial Basis Function Network Radial Basis Function Kernel Nonlinear Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Paweł Strumiłło
    • 1
  • Władysław Kamiński
    • 2
  1. 1.Institute of ElectronicsTechnical University of ŁódźŁódźPoland
  2. 2.Faculty of Process and Environmental EngineeringTechnical University of ŁódźŁódźPoland

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