Implementation of the Relaxation Process by the Interactive Activation and Competition Network

The relaxation process is a useful technique for using contextual information to reduce local ambiguity and achieve global consistency in various applications. It is basically a parallel execution model, adjusting the confidence measures of involved entities based on interrelated hypotheses and confidence measures. On the other hand, the neural network is a computational model with massively parallel execution capability. The output of each neuron depends mainly on the information provided by other neurons. Therefore, there exist certain common properties in the relaxation process and the neural network technique. A mapping method that makes the interactive activation and competition network perform the relaxation process is proposed. By this method, the neural network technology can be easily adapted tb solve the many problems which have already been solved by the relaxation process. Experimental results of solving a modified version of the n-queens problem on the proposed neural network are given to demonstrate the feasibility of the proposed method.