Multi-step-prediction of chaotic time series based

on co-evolutionary recurrent neural network

协同进化递归神经网络的多步混沌时间序列预测-This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic

time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent

neural networks by co-evolutionary strategy.　The searching space was separated into two subspaces and the individuals

are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability

of recurrent neural network to incorporate past experience due to internal recurrence.　The eff ectiveness of CERNN is

evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey–Glass series and real-world

sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic

time series.