Flexible kernels discrete Hopfield neural network

This paper presents a new framework for the development of generalized composite kernels machines for discrete Hopfield neural network and to upgrading the performance of logic programming in Hopfield network by applying kernels machines in the system. We put up a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the analyze data that make use of kernels. The present an analysis based on kernels machines (Linear kernel Hopfield neural network, Polynomial kernel Hopfield neural network, and Gaussian kernel Hopfield neural network) that measure how the capacity of Hopfield network to use for solving the combinatorial problem that always occurs in Hopfield network and guaranty that the solution is optimal. 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 kernels Hopfield neural network in enhancing the performance of the system. By applying kernels Hopfield neural network 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 kernels Hopfield neural network in the system will produce solutions with better quality.