A Tabu based NN Learning Algorithm for Nonlinear Function Approximation

In this paper, a tabu based neural network learning algorithm (TBBP) is represented to improve the function approximation ability of neural networks to nonlinear functions. By using the tabu search during the search process in the global area, the algorithm can escape from the local optimal solution and get a superior global optimization for the neural networks. The TBBP is tested in 6 different nonlinear functions. It is compared with the standard BP algorithm. The results show that the tabu search has improved the ability of the approximating ability of the neural networks