Chaotic neural network with nonlinear self-feedback and its application in optimization

When a special nonlinear self-feedback is introduced into the Hopfield model, the network becomes a chaotic one. Chaotic dynamics of the system can prevent its state from staying at local minima of the energy indefinitely. The system then gets the ability to transfer chaotically among local minima, which can be employed to solve optimization problems. With autonomous adjustment of the parameters, the system can realize the global optimal solution eventually or approximately with transient chaos. Simulations on the Traveling Salesman Problem (TSP) have shown that the proposed chaotic neural network can converge to the global minimum or its approximate solutions more efficiently than the Hopfield network.