Introduction
Radial basis networks can require more neurons than standard feedforward backpropagation networks, but often they can be designed in a fraction of the time it takes to train standard feedforward networks. They work best when many training vectors are available.
You might want to consult the following paper on this subject: Chen, S., C.F.N. Cowan, and P.M. Grant, "Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks," IEEE Transactions on Neural Networks, Vol. 2, No. 2, March 1991, pp. 302-309.
This chapter discusses two variants of radial basis networks, generalized regression networks (GRNN) and probabilistic neural networks (PNN). You can read about them in P.D. Wasserman, Advanced Methods in Neural Computing, New York: Van Nostrand Reinhold, 1993, on pp. 155-61 and pp. 35-55, respectively.
Important Radial Basis Functions
Radial basis networks can be designed with either newrbe or newrb. GRNNs and PNNs can be designed with newgrnn and newpnn, respectively.
Type help radbasis to see a listing of all functions and demonstrations related to radial basis networks.
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