Acceleration Technique for Neuro Symbolic Integration
This paper presents an improved technique for accelerating the process of doing logic programming in discrete Hopfield neural network by integrating fuzzy logic and modifying activation function. Generally Hopfield networks are suitable for solving combinatorial optimization problems and pattern recognition problems. However Hopfield neural networks also face some limitations; one of the major limitation is the solutions are local minima rather than global minima. Hereby, we introduce an improved technique by integrating Hopfield network, modifying activation function and fuzzy logic technique to have better energy relaxation and global solutions. Computer simulations are carried out to verify and validate the proposed approach.
Neuro-Symbolic Integration is an interdisciplinary area that endeavours to unify neural networks and symbolic logic. The goal is to create a system that combines the advantages of neural networks (adaptive behaviour, robustness, tolerance of noise and probability) and symbolic logic (validity of computations, generality, higherorder reasoning). Several different approaches have been proposed in...متن کامل
The Neuro-Symbolic Hybrid Systems (NSHS) are used to solve problems where there exists a necessity of combining and integrating the artificial neural networks and the symbolic representations in only one system in order to obtain better results. We developed a NSHS Methodology to integrate the knowledge of a human expert and the numeric knowledge obtained from a computer vision process. We impl...متن کامل
It has been one of the great challenges of neuro-symbolic integration to represent recursive logic programs using neural networks of finite size. In this paper, we propose to implement neural networks that can process recursive programs viewed as inductive definitions.متن کامل
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 mi...متن کامل
There is an obvious tension between symbolic and subsymbolic theories, because both show complementary strengths and weaknesses in corresponding applications and underlying methodologies. The resulting gap in the foundations and the applicability of these approaches is theoretically unsatisfactory and practically undesirable. We sketch a theory that bridges this gap between symbolic and subsymb...متن کامل