Guo, Yueling
(2024)
Y-type Random 2-satisfiability In Discrete Hopfield Neural Network.
PhD thesis, Universiti Sains Malaysia.
Abstract
In the current development of Artificial Intelligence, Satisfiability plays a crucial role as a symbolic language of Artificial Intelligence for the transparency of black box models. However, the main problem of existing Satisfiability is the lack of combined logical rule, so the benefits of combined logical rule have not yet been investigated. The rule namely Y-Type Random 2-Satisfiability is proposed by combining the systematic and non-systematic logical rule. Next, the newly proposed logical rule as the symbolic instruction was implemented into the Discrete Hopfield Neural Network to govern the neurons of the network. Experimental results demonstrated the compatibility of the proposed logical rule and the Discrete Hopfield Neural Network. Additionally, the proposed Hybrid Differential Evolution Algorithm was implemented into the training phase to ensure that the cost function of the Discrete Hopfield Neural Network is minimized. During the retrieval phase, a new activation function and Swarm Mutation were proposed to ensure the diversity of the neuron states. The proposed algorithm and mutation mechanism showed optimal performances as compared to the existing algorithms. Finally, a new logic mining model namely Y-Type Random 2-Satisfiability Reverse Analysis was proposed, which showed optimal performances in terms of several metrics as compared to the existing classification models. The developed logic mining will be used to analyze the Alzheimer's Disease Neuroimaging Initiative dataset.
Actions (login required)
 |
View Item |
