Kernel Machine to Doing Logic Programming In Hopfield Network for Solve Non Horn Problem-3SAT

A neural network which is recognized as artificial neural network is a mathematical model or computational model that tries to simulate the structure and functional aspect of biological neural networks. It can solve complicated recognition solve optimization problems and analysis problems. It is because it composed of huge amount of interconnected neurons to solve specific problems [1]. Hopfield Network is a recurrent neural network investigated by John Hopfield in the early 1980s. Hopfield network serves as a content-addressable memory system with binary threshold units [2]. Logic is deals with false and true while in the logic programming, a set of Non Horn clauses 3 sat that formed by atoms are represented to find the truth values of the atoms in the set. It is using neurons to store the truth value of atoms to write a cost function for minimization when all the clauses are satisfied [3]. Moreover, a bi-directional mapping between propositional logic formulas and energy functions of symmetric neural networks had defined by Gadi Pinkas [4] and Wan Abdullah [5]. Further detail can refer to the references. The advantages by using Wan Abdullah’s method are it can revolves around propositional non Horn clauses 3 sat and learning ability of the Hopfield network and hunts for the best solutions, given the clauses in the logic program, and the corresponding solutions may change as new clauses added. This research focus in kernel Hopfield neuron network, Kernel machine are powerful, computational effective analytical tools that are qualified for working on high dimensional data with complex structure [6]. In kernel methods, the data are mapped from their original space to a higher dimensional feature space [7]. Any operation, that can be represented through dot products has a kernel evaluation and called kernelization [8,9]. The rest of the paper is organized as follows