Applying Fuzzy Logic in Neuro Symbolic Integration

This paper presents an improved approach for enhancing the performance of doing logic programming in Hopfield neural network. Generally Hopfield networks are suitable for solving combinatorial optimization problems. In spite of usefulness of Hopfield neural networks they have limitations; one of the most concerning drawbacks is that sometimes the solutions are local minimum instead of global minimum solutions. In this paper, we present an improved approach by integration of Hopfield network and fuzzy logic technique to have better energy relaxation and avoid locally minimum solutions. We carried out computer simulations to verify and validate the proposed approach.