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.
Highlights
INTRODUCTIONThe field of neuro-symbolic integration is stimulated by the fact that formal theories (as studied in mathematical logic and used in automated reasoning) are commonly recognised as deductive systems which lack such properties of human reasoning as adaptation, learning and self-organisation
The field of neuro-symbolic integration is stimulated by the fact that formal theories are commonly recognised as deductive systems which lack such properties of human reasoning as adaptation, learning and self-organisation
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
Summary
The field of neuro-symbolic integration is stimulated by the fact that formal theories (as studied in mathematical logic and used in automated reasoning) are commonly recognised as deductive systems which lack such properties of human reasoning as adaptation, learning and self-organisation. Hopfield network is a recurrent neural network [1] 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. We had developed agent based modelling (ABM) for integration of Boltzmann machine and hyperbolic activation function in higher Order Hopfield Networks. An ABM is a new computational modelling paradigm which is an analysing systems that representing the ‘agents’ that involving and simulating of their interactions. Their attributes and behaviours will be group together through their interactions to become a scale.
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