Biogeography Based Optimization for Weight Optimization in Elman Neural Network Compared with Meta Heuristics Methods
In this paper, we present a learning algorithm for the Elman Recurrent Neural Network (ERNN) based on Biogeography Based Optimization (BBO) The proposed algorithm computes the weights, initials inputs of the context units and self feedback coefficient of the Elman network. The method applied for four benchmark problems: Mackey Glass and Lorentz equations, which produce chaotic time series, and to real life classification; iris and Breast Cancer datasets. Numerical experimental results show improvement of the performance of the proposed algorithm in terms of accuracy and MSE eror over many heuristic algorithms.