Abstract: This chapter focuses on the radial-basis function (RBF) network as an alternative to multilayer perceptrons. It will be interesting to find that in a multilayer perceptron, the function approximation is defined by a nested set of weighted summations, while in a RBF network, the approximation is defined by a single weighted sum. The chapter focuses on the use of a Gaussian function as the radial-basis function. The reason behind the choice of the Gaussian function as the radial-basis function in building RBF networks is that it has many desirable properties, which will become evident as the discussion progresses. It is important to point out that RBF networks and multilayer perceptrons can be trained in alternative ways besides those presented. For multilayer perceptrons, the backpropagation algorithm is simple to compute locally and it performs stochastic gradient descent in weight space when the algorithm is implemented in an online learning mode.
Publication Year: 2016
Publication Date: 2016-06-17
Language: en
Type: other
Indexed In: ['crossref']
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Cited By Count: 4
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