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Chapter 3 Learning Algorithms for RBF Functions and Subspace Based Functions

The renaissance of neural network and then machine learning since the 1980’s is featured by two streams of extensive studies, one on multilayer perceptron and the other on radial basis function networks or shortly RBF net. While a multilayer perceptron partitions data space by hyperplanes and then making subsequent processing via nonlinear transform, a RBF net partitions data space into modular regions via local structures, called radial or non-radial basis functions. After extensive studies on multilayer perceptron, studies have been turned to RBF net since the late 1980’s and the early 1990’s, with a wide range applications (Kardirkamanathan, Niranjan, & Fallside, 1991; Chang & Yang, 1997; Lee, 1999; ABSTRACT