Wavelet chaotic neural network with nonlinear self-feedback and its application to traveling salesman problem

Chaotic neural network has been proved to be a powerful tool to solve combinational optimization problems. Wavelet chaotic neural network is a kind of chaotic neural network with non-monotonous activation function composed by Sigmoid and Wavelet. In this paper, first a wavelet chaotic neural network model with different nonlinear self-feedbacks is proposed and the effects of the different self-feedbacks on simulated annealing are analyzed respectively. Then the proposed model is applied to the typical combinational optimization problem-10-city traveling salesman problem (TSP) and the numerical simulations show that the model can converge to the global optimal or near-optimal solutions more efficiently than the Hopfield network and that the model with Gauss wavelet self-feedback is superior to the model with other self-feedback in performance. Finally, the dynamics of the chaotic neural network model for the 10-city TSP is researched.