Chaotic time series prediction using echo state network based on selective opposition grey wolf optimizer

Chaos prediction of nonlinear system is of great significance for proposing control strategies early. On the other hand, echo state network (ESN) as an artificial neural recursive network is widely used in time series prediction, while it has significant disadvantages due to the random input weight matrix. This work proposes a chaos prediction model based on ESN optimized by the selective opposition grey wolf optimizer (SOGWO). Firstly, the input weight matrix of ESN is restructured to express the position of gray wolf optimizer (GWO). Secondly, the selective opposition strategy is introduced into the GWO to enhance the exploration and exploitation capability. Thirdly, the optimal network is achieved by iteratively updating the position of SOGWO where the input weight is replaced by optimal search agent. Finally, taking two typical chaotic time series of Mackey-Glass and Lorenz as the objects, the effectiveness of the proposed SOGWO-ESN is verified by numerical simulation. The experimental results of SOGWO-ESN are compared with those of the traditional echo state network, the network model optimized by the particle swarm optimization (PSO) and the traditional gray wolf optimizer. The results imply that the proposed SOGWO-ESN has better prediction performance and is able to accurately predict a much longer period of time.

Automated summary

This work proposes a chaos prediction model based on ESN optimized by SOGWO, which achieves better prediction performance and accurately predicts a much longer period of time by optimizing the input weight matrix and introducing an opposition strategy.