Optimizing time series prediction of echo state networks based on war strategy algorithm
BAI Yiran
LUN Shuxian
Abstract：In order to solve the problem of difficult to determine the parameters of the reserve pool of echo state network(ESN),this paper proposes an echo state network model(WSO-ESN)based on the war strategy optimization algorithm(WSO).The model utilizes two popular war strategies,attack and defense,in the war strategy optimization algorithm to better achieve a balance between global exploration and local exploitation of the whole model,and replaces the weak soldier strategy to improve its robustness so that the WSO algorithm will be more accurate in determining the ESN parameters.In addition,an exponentially varying weight updating mechanism is introduced to improve the convergence speed of the algorithm and thus determine the reserve pool parameters faster.The experimental results are compared with those of particle swarm optimization(PSO),dung beetle optimization(DBO),and golden jackal optimization(GJO)for optimizing the reserve pool parameters.The results show that the echo state network model based on war strategy optimization algorithm proposed in this paper has faster training speed and higher prediction accuracy.
Keywords：reserve poolsrobustnessecho-state networks
Publication Date：2024-06-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 154-160 )