Logic Programming In Radial Basis Function Neural Networks
In this thesis, I established new techniques to represent logic programming in radial basis function neural networks. Two techniques were developed. The first technique is to encode the logic programming in radial basis function neural networks. The second technique is to compute the single step ope...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 語言: | English |
| 出版: |
2013
|
| 主題: | |
| 在線閱讀: | http://eprints.usm.my/46181/1/Nawaf%20Hamadneh24.pdf http://eprints.usm.my/46181/ |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| id |
my.usm.eprints.46181 |
|---|---|
| record_format |
eprints |
| spelling |
my.usm.eprints.46181 http://eprints.usm.my/46181/ Logic Programming In Radial Basis Function Neural Networks Hamadneh, Nawaf QA1 Mathematics (General) In this thesis, I established new techniques to represent logic programming in radial basis function neural networks. Two techniques were developed. The first technique is to encode the logic programming in radial basis function neural networks. The second technique is to compute the single step operator of logic programming in radial basis function neural networks. I used different types of optimization algorithms to improve the performance of the neural networks. I used three different techniques for improving the predictive capability of the neural networks. These techniques are: no-training technique, half training technique and full training technique. In this thesis, I established a new method for determining the best number of the hidden neurons in radial basis function neural networks. To do that I used the root mean square error function and Schwarz bayesian criterion as model selection criteria. I used real data sets of different sizes in the computational results. The analysis revealed that performance of particle swarm optimization algorithm and Prey predator algorithm are better to use in training the networks. In this thesis also, I developed a new technique to extract the logic programming from radial basis function neural networks. To do that, I established the radial basis function neural networks which represent the three conjunctive normal form (3-CNF) logic programming. Following this, I implemented the results to represent the electronic circuits in the radial basis function neural networks. 2013-11 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/46181/1/Nawaf%20Hamadneh24.pdf Hamadneh, Nawaf (2013) Logic Programming In Radial Basis Function Neural Networks. PhD thesis, Universiti Sains Malaysia. |
| institution |
Universiti Sains Malaysia |
| building |
Hamzah Sendut Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Sains Malaysia |
| content_source |
USM Institutional Repository |
| url_provider |
http://eprints.usm.my/ |
| language |
English |
| topic |
QA1 Mathematics (General) |
| spellingShingle |
QA1 Mathematics (General) Hamadneh, Nawaf Logic Programming In Radial Basis Function Neural Networks |
| description |
In this thesis, I established new techniques to represent logic programming in radial basis function neural networks. Two techniques were developed. The first technique is to encode the logic programming in radial basis function neural networks. The second technique is to compute the single step operator of logic programming in radial basis function neural networks. I used different types of optimization algorithms to improve the performance of the neural networks. I used three different techniques for improving the predictive capability of the neural networks. These techniques are: no-training technique, half training technique and full training technique. In this thesis, I established a new method for determining the best number of the hidden neurons in radial basis function neural networks. To do that I used the root mean square error function and Schwarz bayesian criterion as model selection criteria. I used real data sets of different sizes in the computational results. The analysis revealed that performance of particle swarm optimization algorithm and Prey predator algorithm are better to use in training the networks. In this thesis also, I developed a new technique to extract the logic programming from radial basis function neural networks. To do that, I established the radial basis function neural networks which represent the three conjunctive normal form (3-CNF) logic programming. Following this, I implemented the results to represent the electronic circuits in the radial basis function neural networks. |
| format |
Thesis |
| author |
Hamadneh, Nawaf |
| author_facet |
Hamadneh, Nawaf |
| author_sort |
Hamadneh, Nawaf |
| title |
Logic Programming In Radial Basis
Function Neural Networks |
| title_short |
Logic Programming In Radial Basis
Function Neural Networks |
| title_full |
Logic Programming In Radial Basis
Function Neural Networks |
| title_fullStr |
Logic Programming In Radial Basis
Function Neural Networks |
| title_full_unstemmed |
Logic Programming In Radial Basis
Function Neural Networks |
| title_sort |
logic programming in radial basis
function neural networks |
| publishDate |
2013 |
| url |
http://eprints.usm.my/46181/1/Nawaf%20Hamadneh24.pdf http://eprints.usm.my/46181/ |
| _version_ |
1662755749832425472 |
| score |
13.251813 |