Comparing the logic programming between Hopfield neural network and radial basis function neural network

Abstract

Logic programming is a superior language because it operates on a higher level of mathematical or logical reasoning. Logic programming is well-suited in building the artificial intelligence systems. In this paper, we reviewed the performance of the logic programming in Hopfield Neural Network (HNN) and Radial Basis Function Neural Network (RBFNN). Logic programming by using the Embedding method will improve the performance of RBFNN. In HNN, the logic programming can be implemented by finding the optimal synaptic weight via Wan Abdullah method. RBFNN is expected to do logic programming optimally compared to HNN. This study gives an overview of HNN and RBFNN regarding architectures, learning processing, and their application in 2 Satisfiability (2SAT) logic programming. Both networks will be assessed based on accuracy, sensitivity, and robustness. Pursuing that, RBFNN is expected to outperform HNN in doing 2 Satisfiability logic programming.Logic programming is a superior language because it operates on a higher level of mathematical or logical reasoning. Logic programming is well-suited in building the artificial intelligence systems. In this paper, we reviewed the performance of the logic programming in Hopfield Neural Network (HNN) and Radial Basis Function Neural Network (RBFNN). Logic programming by using the Embedding method will improve the performance of RBFNN. In HNN, the logic programming can be implemented by finding the optimal synaptic weight via Wan Abdullah method. RBFNN is expected to do logic programming optimally compared to HNN. This study gives an overview of HNN and RBFNN regarding architectures, learning processing, and their application in 2 Satisfiability (2SAT) logic programming. Both networks will be assessed based on accuracy, sensitivity, and robustness. Pursuing that, RBFNN is expected to outperform HNN in doing 2 Satisfiability logic programming.