Efficient matching algorithm by a hybrid Hopfield network for object recognition

Hopfield proposed two types of neural networks; Discrete Hopfield Network (DHN) and Continuous Hopfield Network (CHN). Those have been used for solving the well-known traveling salesman problem in a sense of optimization. DHN, a stochastic model is simple to implement and fast in computing. However, DHN uses binary value for states of neurons and results in an approximate solution. On the other hand, CHN gives a near-optimal solution, but it takes too much time to simulate a differential equation which represents a main characteristic of CHN. A matching problem using a graph matching technique can be cast into an optimization problem. In this paper, a new method for two-dimensional object recognition by using a Hopfield neural network is presented. A Hybrid Hopfield Network (HHN), which combines the merit of both the Continuous Hopfield Network and the Discrete Hopfield Network, is described and some of the advantages such as reliability and speed are shown in this paper. The main idea behind the new network is that stable states of neurons are analyzed and predicted based upon the theory of CHN after the convergence in DHN.