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

## Abstract

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.

- Authors:

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

- Publication Date:

- OSTI Identifier:
- 22306151

- 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; ARTIFICIAL INTELLIGENCE; FUNCTIONS; NEURAL NETWORKS; OPTIMIZATION; PERFORMANCE; PROGRAMMING; TRAINING

### Citation Formats

```
Hamadneh, Nawaf, Sathasivam, Saratha, and Choon, Ong Hong.
```*Computing single step operators of logic programming in radial basis function neural networks*. United States: N. p., 2014.
Web. doi:10.1063/1.4887632.

```
Hamadneh, Nawaf, Sathasivam, Saratha, & Choon, Ong Hong.
```*Computing single step operators of logic programming in radial basis function neural networks*. United States. doi:10.1063/1.4887632.

```
Hamadneh, Nawaf, Sathasivam, Saratha, and Choon, Ong Hong. Thu .
"Computing single step operators of logic programming in radial basis function neural networks". United States.
doi:10.1063/1.4887632.
```

```
@article{osti_22306151,
```

title = {Computing single step operators of logic programming in radial basis function neural networks},

author = {Hamadneh, Nawaf and Sathasivam, Saratha and Choon, Ong Hong},

abstractNote = {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.},

doi = {10.1063/1.4887632},

journal = {AIP Conference Proceedings},

number = 1,

volume = 1605,

place = {United States},

year = {Thu Jul 10 00:00:00 EDT 2014},

month = {Thu Jul 10 00:00:00 EDT 2014}

}