Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 57, Issue 3, Pages 1127-1131Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2009.2027254
Keywords
Biological neural network (BNN); genetic algorithms (GAs); identification; optimization methods
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Funding
- Hong Kong SAR, China [CityU 120708]
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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.
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