Prediction of Sunspot Series Using BiLinear Recurrent Neural Network

Abstract

A prediction scheme of sunspot series using a BiLinear Recurrent Neural Network (BLRNN) is proposed in this paper. Since the BLRNN is based on the bilinear polynomial, it has been successfully used in modeling highly nonlinear systems with time-series characteristics and the BLRNN can be a natural choice in predicting sunspot series. The performance of the proposed BLRNN-based predictor is evaluated and compared with the conventional MultiLayer Perceptron Type Neural Network (MLPNN)-based predictor. Experiments are conducted on the Wolf sunspot series number data. The results show that the proposed BLRNN based predictor outperforms the MLPNN-based one interms of the Normalized Mean Squared Error (NMSE).