A comparison of optimization algorithms for biological neural network identification

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Original languageEnglish
Article number5173522
Pages (from-to)1127-1131
Journal / PublicationIEEE Transactions on Industrial Electronics
Issue number3
Publication statusPublished - Mar 2010


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. © 2006 IEEE.

Research Area(s)

  • Biological neural network (BNN), Genetic algorithms (GAs), Identification, Optimization methods