A hierarchy neural network approach to symbolic logic-algorithms of problem solving

This paper presents a neural network approach, based on high-order two-dimension temporal and dynamically clustering competitive activation mechanism, to implement the parallel searching algorithm and many other symbolic logic algorithms. This approach is superior in many respects to both the common sequential algorithms of symbolic logic and the common neural network used for optimization problems. Simulations for some problem solving prove the effect of the approach.