Comparing the logic programming between Hopfield neural network and radial basis function neural network

Shehab Abdulhabib Alzaeemi; Mohd Asyraf Mansor; Mohd Shareduwan Mohd Kasihmuddin; Saratha Sathasivam

doi:10.1063/1.5136476

Comparing the logic programming between Hopfield neural network and radial basis function neural network

Shehab Abdulhabib Alzaeemi
, Mohd Asyraf Mansor
, Mohd Shareduwan Mohd Kasihmuddin
, Saratha Sathasivam

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Logic programming is a superior language because it operates on a higher level of mathematical or logical reasoning. Logic programming is well-suited in building the artificial intelligence systems. In this paper, we reviewed the performance of the logic programming in Hopfield Neural Network (HNN) and Radial Basis Function Neural Network (RBFNN). Logic programming by using the Embedding method will improve the performance of RBFNN. In HNN, the logic programming can be implemented by finding the optimal synaptic weight via Wan Abdullah method. RBFNN is expected to do logic programming optimally compared to HNN. This study gives an overview of HNN and RBFNN regarding architectures, learning processing, and their application in 2 Satisfiability (2SAT) logic programming. Both networks will be assessed based on accuracy, sensitivity, and robustness. Pursuing that, RBFNN is expected to outperform HNN in doing 2 Satisfiability logic programming.

Original language	English
Title of host publication	Proceedings of the International Conference on Mathematical Sciences and Technology 2018, MathTech 2018
Subtitle of host publication	Innovative Technologies for Mathematics and Mathematics for Technological Innovation
Editors	Yazariah Mohd Yatim, Syakila Ahmad, Mohd Tahir Ismail, Majid Khan Majahar Ali, Rosmanjawati Abdul Rahman, Hajar Sulaiman, Norshafira Ramli, Noor Atinah Ahmad, Farah Aini Abdullah
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735419315
DOIs	https://doi.org/10.1063/1.5136476
State	Published - 4 Dec 2019
Externally published	Yes
Event	1st International Conference on Mathematical Sciences and Technology 2018: Innovative Technologies for Mathematics and Mathematics for Technological Innovation, MathTech 2018 - Penang, Malaysia Duration: 10 Dec 2018 → 12 Dec 2018

Publication series

Name	AIP Conference Proceedings
Volume	2184
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	1st International Conference on Mathematical Sciences and Technology 2018: Innovative Technologies for Mathematics and Mathematics for Technological Innovation, MathTech 2018
Country/Territory	Malaysia
City	Penang
Period	10/12/18 → 12/12/18

Bibliographical note

Publisher Copyright:
© 2019 Author(s).

ASJC Scopus subject areas

General Physics and Astronomy

Access to Document

10.1063/1.5136476

Cite this

Alzaeemi, S. A., Mansor, M. A., Kasihmuddin, M. S. M., & Sathasivam, S. (2019). Comparing the logic programming between Hopfield neural network and radial basis function neural network. In Y. M. Yatim, S. Ahmad, M. T. Ismail, M. K. M. Ali, R. A. Rahman, H. Sulaiman, N. Ramli, N. A. Ahmad, & F. A. Abdullah (Eds.), Proceedings of the International Conference on Mathematical Sciences and Technology 2018, MathTech 2018: Innovative Technologies for Mathematics and Mathematics for Technological Innovation Article 060044 (AIP Conference Proceedings; Vol. 2184). American Institute of Physics Inc.. https://doi.org/10.1063/1.5136476

Alzaeemi, Shehab Abdulhabib ; Mansor, Mohd Asyraf ; Kasihmuddin, Mohd Shareduwan Mohd et al. / Comparing the logic programming between Hopfield neural network and radial basis function neural network. Proceedings of the International Conference on Mathematical Sciences and Technology 2018, MathTech 2018: Innovative Technologies for Mathematics and Mathematics for Technological Innovation. editor / Yazariah Mohd Yatim ; Syakila Ahmad ; Mohd Tahir Ismail ; Majid Khan Majahar Ali ; Rosmanjawati Abdul Rahman ; Hajar Sulaiman ; Norshafira Ramli ; Noor Atinah Ahmad ; Farah Aini Abdullah. American Institute of Physics Inc., 2019. (AIP Conference Proceedings).

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title = "Comparing the logic programming between Hopfield neural network and radial basis function neural network",

abstract = "Logic programming is a superior language because it operates on a higher level of mathematical or logical reasoning. Logic programming is well-suited in building the artificial intelligence systems. In this paper, we reviewed the performance of the logic programming in Hopfield Neural Network (HNN) and Radial Basis Function Neural Network (RBFNN). Logic programming by using the Embedding method will improve the performance of RBFNN. In HNN, the logic programming can be implemented by finding the optimal synaptic weight via Wan Abdullah method. RBFNN is expected to do logic programming optimally compared to HNN. This study gives an overview of HNN and RBFNN regarding architectures, learning processing, and their application in 2 Satisfiability (2SAT) logic programming. Both networks will be assessed based on accuracy, sensitivity, and robustness. Pursuing that, RBFNN is expected to outperform HNN in doing 2 Satisfiability logic programming.",

author = "Alzaeemi, \{Shehab Abdulhabib\} and Mansor, \{Mohd Asyraf\} and Kasihmuddin, \{Mohd Shareduwan Mohd\} and Saratha Sathasivam",

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Alzaeemi, SA, Mansor, MA, Kasihmuddin, MSM & Sathasivam, S 2019, Comparing the logic programming between Hopfield neural network and radial basis function neural network. in YM Yatim, S Ahmad, MT Ismail, MKM Ali, RA Rahman, H Sulaiman, N Ramli, NA Ahmad & FA Abdullah (eds), Proceedings of the International Conference on Mathematical Sciences and Technology 2018, MathTech 2018: Innovative Technologies for Mathematics and Mathematics for Technological Innovation., 060044, AIP Conference Proceedings, vol. 2184, American Institute of Physics Inc., 1st International Conference on Mathematical Sciences and Technology 2018: Innovative Technologies for Mathematics and Mathematics for Technological Innovation, MathTech 2018, Penang, Malaysia, 10/12/18. https://doi.org/10.1063/1.5136476

Comparing the logic programming between Hopfield neural network and radial basis function neural network. / Alzaeemi, Shehab Abdulhabib; Mansor, Mohd Asyraf; Kasihmuddin, Mohd Shareduwan Mohd et al.
Proceedings of the International Conference on Mathematical Sciences and Technology 2018, MathTech 2018: Innovative Technologies for Mathematics and Mathematics for Technological Innovation. ed. / Yazariah Mohd Yatim; Syakila Ahmad; Mohd Tahir Ismail; Majid Khan Majahar Ali; Rosmanjawati Abdul Rahman; Hajar Sulaiman; Norshafira Ramli; Noor Atinah Ahmad; Farah Aini Abdullah. American Institute of Physics Inc., 2019. 060044 (AIP Conference Proceedings; Vol. 2184).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kasihmuddin, Mohd Shareduwan Mohd

AU - Sathasivam, Saratha

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Alzaeemi SA, Mansor MA, Kasihmuddin MSM, Sathasivam S. Comparing the logic programming between Hopfield neural network and radial basis function neural network. In Yatim YM, Ahmad S, Ismail MT, Ali MKM, Rahman RA, Sulaiman H, Ramli N, Ahmad NA, Abdullah FA, editors, Proceedings of the International Conference on Mathematical Sciences and Technology 2018, MathTech 2018: Innovative Technologies for Mathematics and Mathematics for Technological Innovation. American Institute of Physics Inc. 2019. 060044. (AIP Conference Proceedings). doi: 10.1063/1.5136476

Comparing the logic programming between Hopfield neural network and radial basis function neural network

Abstract

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Bibliographical note

ASJC Scopus subject areas

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