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Training recurrent neural networks for dynamic system identification using parallel tabu search algorithm

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2 Author(s)
Karaboga, D. ; Dept. of Electron. Eng., Erciyes Univ., Kayseri, Turkey ; Kalinli, A.

There are several modern heuristic optimisation techniques, such as neural networks, genetic algorithms, simulated annealing and tabu search algorithms. Of these algorithms, the tabu search is quite a new, promising search technique for numeric problems, especially for nonlinear problems. However, the convergence speed of the standard tabu search to the global optimum is initial-solution-dependent, since it is a form of iterative search. In this paper, a new model of tabu searching, which has been proposed by the authors to overcome the drawback of a standard tabu search, is tested for training a recurrent neural network to identify dynamic systems

Published in:

Intelligent Control, 1997. Proceedings of the 1997 IEEE International Symposium on

Date of Conference:

16-18 Jul 1997

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