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

There are several modern heuristic optimisation techniques such as neural networks, genetic algorithms, simulated annealing and tabu search algorithms. Of these algorithms, tabu search is quite new promising search technique for numeric problems, especially for non-linear problems. However, the converging 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 search 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.