Portal de Periódicos da CAPES

Enhanced Hopfield Network for Pattern Satisfiability Optimization

2016; Volume: 8; Issue: 11 Linguagem: Inglês

10.5815/ijisa.2016.11.04

2074-9058

Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Saratha Sathasivam,

Fuzzy Logic and Control Systems

Highly-interconnected Hopfield network with Content Addressable Memory (CAM) are shown to be extremely effective in constraint optimizat ion problem.The emergent of the Hopfield network has producing a prolific amount of research.Recently, 3 Sat isfiability (3-SAT) has becoming a tool to represent a variety combinatorial problems.Incorporated with 3-SAT, Hopfield neural network (HNN-3SAT) can be used to optimize pattern satisfiability (Pattern-SAT) prob lem.Hence, we proposed the HNN-3SAT with Hyperbolic Tangent activation function and the conventional McCulloch-Pitts function.The aim of this study is to investigate the accuracy of the pattern generated by our proposed algorithms.Microsoft Visual C++ 2013 is used as a platform for training, testing and validating our Pattern-SAT design.The detailed performance of HNN-3SAT of our proposed algorithms in doing Pattern-SAT will be discussed based on global pattern-SAT and running time.The result obtained fro m the simulat ion demonstrate the effectiveness of HNN-3SAT in doing Pattern-SAT.