5561733
doi
10.35940/ijeat.D7711.049420
oai:zenodo.org:5561733
Blue Eyes Intelligence Engineering & Sciences Publication(BEIESP)
Publisher
Saratha Sathasivam
School of General and Foundation Studies, AIMST University, 08100 Semeling, Kedah
Muraly Velavan
Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, 21300 Kuala Terengganu, Terengganu.
Agent-based Modeling for Activation Function in Enhancement Logic Programming in Hopfield Neural Network
Shehab Abdulhabib Alzaeemi
Saratha Sathasivam2,*, School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang Malaysia.
issn:2249-8958
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Logic Program, Hopfield Neural Network, Zeng Martinez Activation Function, Hyperbolic Tangent Activations Function, Agent based Modelling.
<p>Artificial Neural Network (ANN) uses many activation functions to update the state on neuron. The research and engineering have been used activation functions in the artificial neural network as the transfer functions. The most common reasons for using this transfer function were its unit interval boundaries, the functions and quick computability of its derivative, and several useful mathematical properties in the approximation of theory realm. Aim of this study is to figure out the best robust activation functions to accelerate HornSAT logic in the Hopfield Neural Network's context. In this paper we had developed Agent-based Modelling (ABM) assessed the performance of the Zeng Martinez Activation Function (ZMAF) and the Hyperbolic Tangent Activation Function (HTAF) beside the Wan Abdullah method to do Logic Programming (LP) in Hopfield Neural Network (HNN). These assessments are carried out on the basis of hamming distance (HD), the global minima ratio (zM), and CPU time. NETLOGO 5.3.1 software has been used for developing Agent-based Modeling (ABM) to test the proposed comparison of the efficaecy of these two activation functions HTAF and ZMAF.</p>
Zenodo
2020-04-30
info:eu-repo/semantics/article
5561732
1633960110.992962
1121773
md5:dbc8fb13727dbc65ade73c3d179e234e
https://zenodo.org/records/5561733/files/D7711049420.pdf
public
2249-8958
Is cited by
issn
International Journal of Engineering and Advanced Technology (IJEAT)
9
4
1872-1879
2020-04-30