The minimum cost path finding algorithm using a Hopfield type neural network

Neural networks have been proposed as new computational tools for solving constrained optimization problems. In this paper the minimum cost path finding algorithm is proposed by using a Hopfield type neural network. In order to design a Hopfield type neural network, an energy function must be defined at first. To achieve this, the concept of a vector-represented network is used to describe the connected path. Through simulations, it will be shown that the proposed algorithm works very well in many cases. The local minima problem of a Hopfield type neural network is discussed