Boltzmann Machine and Hyperbolic Activation Function in Higher Order Network

Saratha Sathasivam, Muraly Velavan


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.

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