Applying Fuzziness in Neural Symbolic-Integration - Farah Liyana, Azizan; Sathasivam, Saratha (School of Mathematical Sciences, USM, 2012)
This paper presents a new approach to upgrade the performance of logic programming in Hopfield network by applying fuzziness in the system. Fuzzy Hopfield neural network clustering technique is used as it can solve the combinatorial optimization problems that always occur in Hopfield network. Neural networks are networks of neurons as the information processing paradigm that is inspired by the way biological nervous system, such as brain, process information while logic describes relationship among propositions. Logic requires descriptive symbolic tools whereas for neural networks are non-symbolic form. By neural-logic integration, the advantages of both neural network and logic programming can be combined. This work is merely focusing on the ways to upgrade the performance of logic programming in Hopfield network. We carried out computer simulations to demonstrate the ability of fuzzy Hopfield neural network clustering technique in enhancing the performance of the system. By applying fuzzy Hopfield neural network clustering technique in the system, it does not only produce better quality solutions but it also can handle the network better even though the complexity increased. Besides that, the system also makes the solutions converge faster. Thus, the presence of this fuzzy Hopfield neural network clustering technique in the system will produce solutions with better quality.