Hysteresis neural networks for a combinatorial optimization problem

Many researchers have proposed combinatorial optimization problem solver by using neural networks. In this paper, we propose a synthesis procedure for hysteresis neural networks whose equilibrium points correspond to an optimal solution of the combinatorial optimization problem. This system does not constrain its energy from decreasing monotonously, namely the output of this system may oscillate. However, our synthesis procedure guarantees that all equilibrium points correspond to an optimal solution of the combinatorial optimization problem. We control the time constant of each hysteresis neuron, and restrain the system from oscillating