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Title: Satisfiability of logic programming based on radial basis function neural networks

In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Authors:
; ; ;  [1]
  1. School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
Publication Date:
OSTI Identifier:
22306155
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1605; Journal Issue: 1; Conference: SKSM21: 21. national symposium on mathematical sciences: Germination of mathematical sciences education and research towards global sustainability, Penang (Malaysia), 6-8 Nov 2013; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ALGORITHMS; ELECTRONIC CIRCUITS; ERRORS; FUNCTIONS; NEURAL NETWORKS; PROGRAMMING