Leaky echo state network based on methane topology applied to time series prediction

Author:

Gong Yuanpeng1,Lun Shuxian2ORCID,Li Ming2,Yin Hang2

Affiliation:

1. College of Mathematical Sciences Bohai University Jinzhou China

2. College of Control Science and Engineering Bohai University Jinzhou China

Abstract

AbstractIn this paper, a proposed echo state network (ESN) model called F‐ESN is based on methane topology. Due to the neurons in the reservoir of the ESN model being random and sparsely connected, the stability of the system structure could be better, and the prediction accuracy could be higher. To solve this issue, the reservoir's neuronal connectivity is modified to a certain methane structure. A master neuron governs each little unit and communicates with the others to pass information. In addition, MFO optimized by adaptive dynamic operators is used to optimize the three parameters of ESN : leakage rate (a), spectral radius of the connection weight matrix (ρ), and input scale factor . To verify the effectiveness of the proposed method, low‐frequency sin time series, high‐frequency SIN time series, and MG time series are simulated. The experimental results show that the methane topology can further improve the prediction accuracy of leakage echo state networks.

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Control and Optimization,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering

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