Next Article in Journal
Applied Cleaning Methods of Oil Residues from Industrial Tanks
Next Article in Special Issue
Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network
Previous Article in Journal
The Effect of Geometrical, Operational, Mixing Methods, and Rheological Parameters on Discharge Coefficients of Internal-Mixing Twin-Fluid Atomizers
Previous Article in Special Issue
The Integration of Collaborative Robot Systems and Their Environmental Impacts
Open AccessArticle

Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

1
School of Mathematical Sciences, Universiti Sains Malaysia, Penang 11800 USM, Malaysia
2
School of Distance Education, Universiti Sains Malaysia, Penang 11800 USM, Malaysia
*
Author to whom correspondence should be addressed.
Processes 2020, 8(5), 568; https://doi.org/10.3390/pr8050568
Received: 29 January 2020 / Revised: 28 March 2020 / Accepted: 7 April 2020 / Published: 11 May 2020
(This article belongs to the Special Issue Neural Computation and Applications for Sustainable Energy Systems)
Election Algorithm (EA) is a novel variant of the socio-political metaheuristic algorithm, inspired by the presidential election model conducted globally. In this research, we will investigate the effect of Bipolar EA in enhancing the learning processes of a Hopfield Neural Network (HNN) to generate global solutions for Random k Satisfiability (RANkSAT) logical representation. Specifically, this paper utilizes a bipolar EA incorporated with the HNN in optimizing RANkSAT representation. The main goal of the learning processes in our study is to ensure the cost function of RANkSAT converges to zero, indicating the logic function is satisfied. The effective learning phase will affect the final states of RANkSAT and determine whether the final energy is a global minimum or local minimum. The comparison will be made by adopting the same network and logical rule with the conventional learning algorithm, namely, exhaustive search (ES) and genetic algorithm (GA), respectively. Performance evaluation analysis is conducted on our proposed hybrid model and the existing models based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), and Mean Absolute Error (MAPE). The result demonstrates the capability of EA in terms of accuracy and effectiveness as the learning algorithm in HNN for RANkSAT with a different number of neurons compared to ES and GA. View Full-Text
Keywords: Hopfield neural network; election algorithm; random k satisfiability; genetic algorithm; exhaustive search Hopfield neural network; election algorithm; random k satisfiability; genetic algorithm; exhaustive search
Show Figures

Figure 1

MDPI and ACS Style

Sathasivam, S.; Mansor, M.A.; Kasihmuddin, M.S.M.; Abubakar, H. Election Algorithm for Random k Satisfiability in the Hopfield Neural Network. Processes 2020, 8, 568. https://doi.org/10.3390/pr8050568

AMA Style

Sathasivam S, Mansor MA, Kasihmuddin MSM, Abubakar H. Election Algorithm for Random k Satisfiability in the Hopfield Neural Network. Processes. 2020; 8(5):568. https://doi.org/10.3390/pr8050568

Chicago/Turabian Style

Sathasivam, Saratha; Mansor, Mohd. A.; Kasihmuddin, Mohd S.M.; Abubakar, Hamza. 2020. "Election Algorithm for Random k Satisfiability in the Hopfield Neural Network" Processes 8, no. 5: 568. https://doi.org/10.3390/pr8050568

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop