A comparison of optimization algorithms for biological neural network identification / Yin, J.J. in IEEE transactions on industrial electronics, Vol. 57 N° 3 (Mars 2010)  

Public ISBD [article]  inIEEE transactions on industrial electronics > Vol. 57 N° 3 (Mars 2010) . - pp. 1127 - 1131 Titre : A comparison of optimization algorithms for biological neural network identification Type de document : texte imprimé Auteurs : Yin, J.J., Auteur ; Tang, Wallace K. S., Auteur ; Man, K. F., Auteur Article en page(s) : pp. 1127 - 1131 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Biological neural network (BNN) Genetic algorithms (GAs) Identification Optimization methods Résumé : 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. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5173522 [article] A comparison of optimization algorithms for biological neural network identification [texte imprimé] / Yin, J.J., Auteur ; Tang, Wallace K. S., Auteur ; Man, K. F., Auteur . - pp. 1127 - 1131. Génie électrique Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 57 N° 3 (Mars 2010) . - pp. 1127 - 1131 Mots-clés : Biological neural network (BNN) Genetic algorithms (GAs) Identification Optimization methods Résumé : 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. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5173522

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