International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 8, No. 10, 8 Oct. 2016
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Accelerating Activation Function for 3-Satisfiability Logic Programming
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Author(s)
Index Terms
3-Satisfiability, Hyperbolic tangent activation function, Elliot symmetric activation function, McCulloch-Pitts function, Logic programming, Hopfield neural network
Abstract
This paper presents the technique for accelerating 3-Satisfiability (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 optimization 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 compare the performance of proposed activation functions with a conventional function, namely McCulloch-Pitts function. In this study, we evaluate the performances between these functions through computer simulations. Microsoft Visual C++ 2013 was used as a platform for training, validating and testing of the network. We restrict our analysis to 3-Satisfiability (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.
Cite This Paper
Mohd Asyraf Mansor, Saratha Sathasivam, "Accelerating Activation Function for 3-Satisfiability Logic Programming", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.10, pp.44-50, 2016. DOI:10.5815/ijisa.2016.10.05
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