Introduction - If you have any usage issues, please Google them yourself
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.