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Erschienen in:
2013 | OriginalPaper | Buchkapitel
A Fully Complex-valued Radial Basis Function Network and Its Learning Algorithm
verfasst von : Sundaram Suresh, Narasimhan Sundararajan, Ramasamy Savitha
Erschienen in: Supervised Learning with Complex-valued Neural Networks
Verlag: Springer Berlin Heidelberg
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Radial basis function networks are the most popular neural network architecture due its simpler structure and better approximation ability owing to the localization property of the Gaussian function. in this chapter, we study complex-valued RBF networks and their learning algorithms. First, we present a complex-valued RBF network which is a direct extension of the real-valued RBF network. CRBF network is a single hidden layer networkwhich computes the output of the network as a linear combination of the hidden neuron outputs.