A Comparison of Optimization Algorithms for Biological Neural Network Identification

Author

Yin, J.J. ; Tang, Wallace K S ; Man, K.F.

Author_Institution

Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, China

Volume

57

Issue

3

fYear

2010

fDate

3/1/2010 12:00:00 AM

Firstpage

1127

Lastpage

1131

Abstract

Recently, the identification of biological neural networks has been reformulated as an optimization problem based on a framework of adaptive synchronization. In this paper, four different optimization algorithms, including genetic algorithm, jumping gene genetic algorithm (JGGA), tabu search, and simulated annealing, have been applied for this optimization problem. Based on the simulation results, their performances are compared, and it is concluded that JGGA can outperform the other three methods in term of minimizing the synchronization and parameter estimation errors.

Keywords

genetic algorithms; neural nets; optimisation; synchronisation; JGGA; adaptive synchronization; biological neural network identification; genetic algorithm; jumping gene genetic algorithm; optimization algorithms; optimization problem; simulated annealing; synchronization; tabu search; Biological neural network (BNN); genetic algorithms (GAs); identification; optimization methods;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

Conference_Location

7/24/2009 12:00:00 AM

ISSN

0278-0046

Type

jour

DOI

10.1109/TIE.2009.2027254

Filename

5173522