The effects of learning mechanism on a special satisfiability logic in discrete Hopfield neural network
Yuan Gao, Mohd Shareduwan Mohd Kasihmuddin, Mohd. Asyraf Mansor, Siti Zulaikha Mohd Jamaludin, Nur Ezlin Zamri · AIP conference proceedings · 2024
The efficiency of learning algorithm can be improved by introducing propositional satisfiability in Discrete Hopfield Neural Network and modeling the neuron structure of the neural network.To improve the flexibility of logic structure and to meet the requirements of all combinatorial problems, a special satisfiability logic structure is introduced in Discrete Hopfield Neural Network.In this paper, Exhaustive Search was harnessed as the learning algorithm to search the neuron fitness, and a constant learning trial was conducted for the sake of implementation.In order to study the learning mechanism on the special satisfiability logic, we focused on the influence of different learning trials on the structure and evaluated the performance of the model at different phases following the evaluation of indicators.We also analyzed the effect of different learning trials on synaptic weights in learning phase and discussed the quantity and quality of global solutions obtained by the model in testing phase.It has been suggested that the learning mechanism was that the higher the trial value, the better the performance in the learning phase and the testing phase.From the perspective of solution diversity, the learning mechanism embodies better solution diversity in fewer trials.