Abstract
For higher-order programming, higher-order network architecture is necessary to provide faster convergence rate, greater storage capacity, stronger approximation property, and higher fault tolerance than lower-order neural networks. Thus, the higher-order clauses for logic programming in Hopfield Networks have been considered in this paper. The goal is to perform logic programming based on the energy minimization scheme is to achieve the best global minimum. However, there is no guarantee to find the best minimum in the network. However, by using Boltzmann Machines and hyperbolic tangent activation function this problem can be overcome.
Highlights
Neural Networks is a mathematical model or computational model that is inspired by the structure of biological neurons such as the brain process information
Logistic function which was frequently in use in neural network, introduced by McCulloch-Pitts where it is already established in original method of doing logic programming in Hopfield network proposed by Wan Abdullah
We want to figure out the maximum complexity that can be reached when higher order clause was applied in logic programming
Summary
Neural Networks is a mathematical model or computational model that is inspired by the structure of biological neurons such as the brain process information. It can solve sophisticated recognition and analysis problems. Hopfield network is a recurrent neural network (Hopfield, 1982) invented by John Hopfield, consists of a set of N interconnected neurons which all neurons are connected to all others in both directions. It has synaptic connection pattern which involving Lyapunov function E (energy function) for dynamic activities.
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