Satisfiability Logic Programming Incorporating Metaheuristics In Hopfield Neural Networks

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Date
2017-08
Authors
Mohd Kasihmuddin, Mohd Shareduwan
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Publisher
Universiti Sains Malaysia
Abstract
The representation of 2 Satisfiability problem or 2SAT is increasingly viewed as a significant logical rule in order to synthesize many real life applications. Although there were many researchers proposed the solution of 2SAT, little attention has been paid to the significance of the 2SAT logical rule itself. It can be hypothesized that, 2SAT property can be used as a logical rule in the intelligent system. To verify this claim, different 2 Satisfiability logic programming namely 2SAT, maximum 2 Satisfiability (MAX2SAT) and Pattern Satisfiability (Pattern-SAT) were embedded to Hopfield neural network (HNN) as a single unit. Maximum 2 Satisfiability is a type of 2SAT logical rule that is unsatisfiable in nature. On the other hand, Pattern-SAT is a new perspective of doing logic programming in HNN-2SAT by helping the user to represent the pattern in terms of 2SAT programming. Learning in HNN will be inspired by Wan Abdullah method since the conventional Hebbian learning is inefficient when dealing with large number of constraints. As the number of 2SAT clauses increased, the efficiency and effectiveness of the learning phase in HNN deteriorates. Inspired by evolutionary algorithm and swarm intelligence metaheuristic algorithm has been introduced to reduce the learning complexity of the network. There are two newly improved metaheuristic algorithms were proposed namely, enhanced genetic algorithm (GA) and enhanced artificial bee colony (ABC) algorithm. These two algorithm will combine with HNN-2SAT model as single unit namely HNN-2SATGA and HNN- 2SATABC. The performance of the all models will be tested by using simulated data set. In terms of real life application, the newly improved 2 satisfiability based Reverse Analysis Method (2SATRA) will be implemented to do logic mining in 16 different real life data sets ranging from finance to medical field. 2SATRA will implement all the proposed hybrid HNN-2SAT models during learning phase. The performance comparison indicates that most of the time, HNN-2SATABC with its variant outperformed other hybrid HNN-2SAT models in doing simulated and real data set.
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Keywords
Satisfiability logic programming Incorporating metaheuristics , In Hopfield Neural Networks.
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