A self-regulated learning in Fully Complex-valued Radial Basis Function Networks

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 training samples. For most real-world problems, this assumption may not be valid. Hence, one needs to develop a learning algorithm which can select proper samples for learning. This paper presents a self-regulatory system that selects samples for learning in each epoch of the batch learning scheme. The algorithm focuses on learning samples with higher errors in the same epoch, deleting samples with smaller errors from the training data set. If the samples do not satisfy both these conditions, they are neither learnt nor deleted but will be used in the next epoch for learning. As this system avoids repeated learning of similar samples, it improves the generalization performance of the FC-RBF network with a lesser computational effort. Performance studies on benchmark problems clearly show the superiority of the proposed algorithm.