A novel chaotic neural network with anti-trigonometric function self-feedback

A Chaotic neural network model with anti-trigonometric function self-feedback is proposed by introducing anti-trigonometric function into self-feedback of chaotic neural network The analyses of the optimization mechanism of the networks suggest that anti-trigonometric function self-feedback affects the original Hopfield energy function in the manner of the sum of the multiplications of anti-trigonometric function to the state, avoiding the network being trapped into the local minima. The energy function is constructed, and the sufficient condition for the networks to reach asymptotical stability is analyzed and is used to instruct the parameter set of the networks for solving traveling salesman problem (TSP). Simulation research on function optimization and TSP indicates that the proposed networks can find the optimal solution of combinatorial optimization problems.