Regularized dynamic self organized neural network inspired by the immune algorithm for financial time series prediction

Abstract

A novel type of recurrent neural network, the regularized Dynamic Self Organised Neural Network Inspired by the Immune Algorithm, is presented. The Regularization technique is used with the Dynamic self-organized multilayer perceptrons network that is inspired by the immune algorithm. The regularization has been addressed to improve the generalization and to solve the over-fitting problem. The results of an average 30 simulations generated from ten stationary signals are demonstrates. The results of the proposed network were compared with the regularized multilayer neural networks and the regularized self organized neural network inspired by the immune algorithm. The simulation results indicated that the proposed network showed better values in terms of the annualized return in comparison to the benchmarked networks. © 2014 Springer International Publishing Switzerland.