Computing single step operators of logic programming in radial basis function neural networks

Journal Article · Thu Jul 10 04:00:00 EDT 2014 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.4887632· OSTI ID:22306151

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong ^[1]

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1. School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

• OSTI ID: 22306151
• Journal Information: AIP Conference Proceedings, Journal Name: AIP Conference Proceedings Journal Issue: 1 Vol. 1605; ISSN APCPCS; ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
ALGORITHMS
ARTIFICIAL INTELLIGENCE
FUNCTIONS
NEURAL NETWORKS
OPTIMIZATION
PERFORMANCE
PROGRAMMING
TRAINING