Solving Optimization Problems Based on Chaotic Neural Network with Hysteretic Activation Function

摘要

We present a new chaotic neural network whose neuron activation function is hysteretic, called hysteretic transiently chaotic neural network (HTCNN) and with this network, a combinatorial optimization problem is solved. By using hysteretic activation function which is multi-valued, has memory, is adaptive, HTCNN has higher ability of overcoming drawbacks that suffered from the local minimum. We proceed to prove Lyapunov stability for this new model, and then solve a combinatorial optimization problem-Assignment problems. Numerical simulations show that HTCNN has higher ability to search for globally optimal and has higher searching efficiency.