Hysteresis cellular neural networks for solving combinatorial optimization problems

Hysteresis cellular neural networks are one of artificial neural networks which work effectively against large scale problems. In the previous work, remarkable methods have never been developed to overcome the defects of hysteresis cellular neural networks. We then propose a novel architecture for combinatorial optimization problems to overcome them. Experimental results indicate the efficiency of the architecture.