Maximum 2-satisfiability in radial basis function neural network

Shehab Abdulhabib Alzaeemi, and Saratha Sathasivam, and Mohd Shareduwan Mohd Kasihmuddin, and Mohd. Asyraf Mansor, (2020) Maximum 2-satisfiability in radial basis function neural network. Journal of Quality Measurement and Analysis, 16 (1). pp. 107-115. ISSN 1823-5670


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Maximum k-Satisfiability (MAX-kSAT) is the logic to determine the maximum number of satisfied clauses. Correctly, this logic plays a prominent role in numerous applications as a combinatorial optimization logic. MAX2SAT is a case of MAX-kSAT and is written in Conjunctive Normal Form (CNF) with two variables in each clause. This paper presents a new paradigm in using MAX2SAT by implementing in Radial Basis Function Neural Network (RBFNN). Hence, we restrict the analysis to MAX2SAT clauses. We utilize Dev C++ as the platform of training and testing our proposed algorithm. In this study, the effectiveness of RBFNN-MAX2SAT can be estimated by evaluating the proposed models with testing data sets. The results obtained are analysed using the ratio of satisfied clause (RSC), the root means square error (RMSE), and CPU time. The simulated results suggest that the proposed algorithm is effective in doing MAX2SAT logic programming by analysing the performance by obtaining lower Root Mean Square Error, high ratio of satisfied clauses and lesser CPU time.

Item Type:Article
Keywords:MAX-kSAT; Conjunctive Normal Form; Ratio of satisfied clause; Combinatorial optimisation
Journal:Journal of Quality Measurement and Analysis
ID Code:15097
Deposited By: ms aida -
Deposited On:19 Aug 2020 03:06
Last Modified:25 Aug 2020 01:00

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