Novel random k Satisfiability for k ≤ 2 in hopfield neural network

Saratha Sathasivam, and Mohd. Asyraf Mansor, and Ahmad Izani Md Ismail, and Siti Zulaikha Mohd Jamaludin, and Mohd Shareduwan Mohd Kasihmuddin, and Mustafa Mamat, (2020) Novel random k Satisfiability for k ≤ 2 in hopfield neural network. Sains Malaysiana, 49 (11). pp. 2847-2857. ISSN 0126-6039


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The k Satisfiability logic representation (kSAT) contains valuable information that can be represented in terms of variables. This paper investigates the use of a particular non-systematic logical rule namely Random k Satisfiability (RANkSAT). RANkSAT contains a series of satisfiable clauses but the structure of the formula is determined randomly by the user. In the present study, RANkSAT representation is successfully implemented in Hopfield Neural Network (HNN) by obtaining the optimal synaptic weights. We focus on the different regimes for k ≤ 2 by taking advantage of the non-redundant logical structure, thus obtaining the final neuron state that minimizes the cost function. We also simulate the performances of RANkSAT logical rule using several performance metrics. The simulated results suggest that the RANkSAT representation can be embedded optimally in HNN and that the proposed method can retrieve the optimal final state.

Item Type:Article
Keywords:Artificial neural network; Hopfield neural network; Logic programming; Random satisfiability
Journal:Sains Malaysiana
ID Code:16014
Deposited By: ms aida -
Deposited On:16 Dec 2020 12:47
Last Modified:17 Dec 2020 05:18

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