Enhanced Hopfield Neural Networks With Artificial Immune System Algorithm For Satisfiability Logic Programming

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dc.contributor.authorMansor, Mohd. Asyraf
dc.date.accessioned2019-08-20T02:07:11Z
dc.date.available2019-08-20T02:07:11Z
dc.date.issued2017-08
dc.description.abstractThe emergence of 3-Satisfiability (3-SAT) problem has produced a prolific number of works devoted to the field of logic and data mining. In this study, a new hybrid method in doing logic programming by incorporating 3-SAT logical rules as a computational tool will be presented. Hence, a robust intelligence system that integrates the Hopfield neural network and metaheuristic paradigm is constructed to extract the data set hidden knowledge in the form of 3-Satisfiability logical rule. A hybrid network called HNN-3SATAIS is proposed by assimilating the Hopfield neural network with the enhanced artificial immune system (AIS) algorithm as a training tool in doing 3-Satisfiability logic programming. The performance of the proposed network, HNN-3SATAIS is compared with a modified genetic algorithm with Hopfield neural network (HNN-3SATGA) and the exhaustive search with Hopfield neural network (HNN-3SATES). Theoretically, the proposed HNN-3SATAIS technique is expected to reduce the complexity of the network due to the systematic searching mechanism. In addition, HNN-3SATAIS is a robust method since the metaheuristic algorithm will boost the searching process that will drive to more feasible global solutions. The performances of the hybrid techniques were tested by using simulated and real data set. Dev-C++ Version 5.11 for Windows 10 was used as a platform for training, simulating and validating the performances of the proposed network. Hence, the appraisal of hybrid computational models was conducted experimentally by using the randomized 3-SAT instances and 15 real data sets archived from the UCI machine learning repository. 15 real data sets of different sizes are selected from different fields, ranging from the finances, astronomy to the vigilant diseases data sets.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/8635
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectEnhanced hopfield neural networks withen_US
dc.subjectartificial immune system algorithmen_US
dc.titleEnhanced Hopfield Neural Networks With Artificial Immune System Algorithm For Satisfiability Logic Programmingen_US
dc.typeThesisen_US
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