Boltzmann Machine and Hyperbolic activation function in Higher Order Neuro Symbolic Integration
Muraly Velavan, Zainor Ridzuan bin Yahya, Mohamad Nazri bin Abdul Halif, Saratha Sathasivam
Volume 1: Issue 2, Revised on – 30 June 2020, pp 63-69
Zainor Ridzuan bin Yahya1
Mohamad Nazri bin Abdul Halif2
Abstract:-Higher-order network structure isimportant in doing higher order programming because high-order neural networks have converge faster and have a higher memory and story capacity. Furthermore higher order networks also have higher approximation ability and robust if compare lower-order neural networks. Thus, the higher-order clauses for logic programming in Hopfield Networks are been focused in this paper. We will limit till fifth order network due to complexity issue. Hereby we employed Boltzmann Machines and hyperbolic tangent activation function to increased the performance of neuro symbolic integration. We used agent based modelling to model this problem.
Index Terms:-Boltzmann machine, agent based modelling and hyperbolic tangent activation function
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