Accelerating Activation Function for 3- Satisfiability Logic Programming

This paper presents the technique for accelerating 3-Sat isfiability (3-SAT) logic programming in Hopfield neural network. The core impetus for this work is to integrate activation function for doing 3-SAT logic programming in Hopfield neural network as a single hybrid network. In logic programming, the activation function can be used as a dynamic post optimizat ion paradigm to transform the activation level of a unit (neuron) into an output signal. In this paper, we proposed Hyperbolic tangent activation function and Elliot symmetric activation function. Next, we co mpare the performance of p roposed activation functions with a conventional function, namely McCu lloch-Pitts function. In this study, we evaluate the performances between these functions through computer simu lations. Microsoft Visual C++ 2013 was used as a platform for training, validating and testing of the network. We restrict our analysis to 3-Satisfiab ility (3-SAT) clauses. Moreover, evaluations are made between these activation functions to see the robustness via aspects of global solutions, global Hamming distance, and CPU time.