Identification of Hindmarsh-Rose neuron networks using GEO metaheuristic

Authors:
Lihe Wang;Genke Yang;Lam Fat Yeung
Affiliations:
Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China;Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China;Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Year:
2011

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Abstract

In the last few years bio-inspired neural networks have interested an increasing number of researchers. In this paper, a novel approach is proposed to solve the problem of identifying the topology and parameters in Hindmarsh-Rose-neuron networks. The approach introduces generalized extremal optimization (GEO), a relatively new heuristic algorithm derived from co-evolution to solve the identification problem. Simulation results show that the proposed approach compares favorably with other heuristic algorithms based methods in existing literatures with smaller estimation errors. And it presents satisfying results even with noisy data.