A tabu based neural network learning algorithm

This paper presents a novel neural network learning algorithm, the tabu-based neural network learning algorithm (TBBP). In our work, the TBBP mainly use the tabu search (TS) to improve the nonlinear function approximating ability of the neural network. By using the TS in the global search, the algorithm can escape from the local minima and obtain some superior global solutions, the weights of the neural network, to approximate the nonlinear function. Results confirm that the TBBP can greatly improve the approximating ability of the neural network for several typical nonlinear functions.