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In this paper, we present an efficient learning algorithm for a Fully Complex-valued Radial Basis Function (FC-RBF) Network using a self-regulatory system. One of the important issues in gradient descent learning algorithm for complex-valued network is the proper selection of training data sequence. In general, it is assumed that the training data is uniformly distributed in the input space with non-recurrent...
In this paper, a fully complex radial basis function (FC-RBF) network and a gradient descent learning algorithm are presented. Many complex-valued RBF learning algorithms have been presented in the literature using a split-complex network which uses a real activation function in the hidden layer, i.e., the activation function in these network maps Cn rarr R. Hence these algorithms do not consider...
In a fully complex-valued feed-forward network, the convergence of the complex-valued back-propagation learning algorithm depends on the choice of the activation function, minimization criterion, initial weights and the learning rate. The minimization criteria used in the existing learning algorithms do not approximate the phase well in complex-valued function approximation problems. This aspect is...
A new sequential growing and pruning algorithm for RBF networks, referred to as GAP-RBF algorithm, has been proposed in our paper[1]. GAP-RBF algorithm modifies the growth criterion of Platt’s RAN and combines it with a new pruning strategy. Both the growing and pruning strategy is based on the link between the learning accuracy and the "significance" of the "nearest" or intentionally...
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