Prediction of chaotic time series based on the recurrent predictor neural network

Chaos limits predictability so that the long-term prediction of chaotic time series is very difficult. The main purpose of this paper is to study a new methodology to model and predict chaotic time series based on a new recurrent predictor neural network (RPNN). This method realizes long-term prediction by making accurate multistep predictions. This RPNN consists of nonlinearly operated nodes whose outputs are only connected with the inputs of themselves and the latter nodes. The connections may contain multiple branches with time delays. An extended algorithm of self-adaptive back-propagation through time (BPTT) learning algorithm is used to train the RPNN. In simulation, two performance measures [root-mean-square error (RMSE) and prediction accuracy (PA)] show that the proposed method is more effective and accurate for multistep prediction. It can identify the systems characteristics quite well and provide a new way to make long-term prediction of the chaotic time series.