| Summary: | Radial basis function networks (RBFNs) have
gained widespread appeal amongst researchers and have
shown good performance in a variety of application domains.
They have potential for hybridization and demonstrate
some interesting emergent behaviors. This paper
aims to offer a compendious and sensible survey on RBF
networks. The advantages they offer, such as fast training
and global approximation capability with local responses,
are attracting many researchers to use them in diversified
fields. The overall algorithmic development of RBF networks
by giving special focus on their learning methods,
novel kernels, and fine tuning of kernel parameters have
been discussed. In addition, we have considered the recent
research work on optimization of multi-criterions in
RBF networks and a range of indicative application areas
along with some open source RBFN tools.
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