Parameter Optimization in a Leaky Integrator Echo State Network with an Improved Gravitational Search Algorithm

Shuxian Lun (), Zhenqian Zhang, Ming Li and Xiaodong Lu ()
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Shuxian Lun: School of Control Science and Engineering, Bohai University, Jinzhou 121013, China
Zhenqian Zhang: School of Control Science and Engineering, Bohai University, Jinzhou 121013, China
Ming Li: School of Control Science and Engineering, Bohai University, Jinzhou 121013, China
Xiaodong Lu: School of Information Engineering, Suqian University, Suqian 223800, China

Mathematics, 2023, vol. 11, issue 6, 1-16

Abstract: In the prediction of a nonlinear time series based on a leaky integrator echo state network (leaky-ESN), building a reservoir related to the specific problem is a key step. For problems such as poor performance of randomly generated reservoirs, it is tough to determine the parameter values of the reservoirs. The work in this paper uses the gravitational search algorithm (GSA) to optimize the global parameters of a leaky-ESN, such as the leaking rate, the spectral radius, and the input scaling factor. The basic GSA has some problems, such as slow convergence and poor balance between exploration and exploitation, and it cannot solve some complex optimization problems well. To solve these problems, an improved gravitational search algorithm (IGSA) is proposed in this paper. First, the best agent and elite agents were archived and utilized to accelerate the exploration phase and improve the convergence rate in the exploitation phase. Second, to improve the effect of the poor fitness agents on the optimization result, a differential mutation strategy was proposed, which generated new individuals to replace original agents with worse fitness, increasing the diversity of the population and improving the global optimization ability of the algorithm. Finally, two simulation experiments showed that the leaky-ESN optimized by the IGSA had better prediction accuracy.

Keywords: leaky integrator echo state network (leaky-ESN); gravitational search algorithm (GSA); differential mutation; time series prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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