Implementation Of Boltzmann Machine In Neuro-Symbolic Integration

dc.contributor.author	Teoh, Yeong Kin
dc.date.accessioned	2016-11-23T02:36:22Z
dc.date.available	2016-11-23T02:36:22Z
dc.date.issued	2010-04
dc.description.abstract	In this dissertation, we present and compare two methods of doing logic program on a Hopfie1d network based on the energy minimization scheme. The proposed method is based on the Boltzmann Machine model. Computer simulations to demonstrate the ability of Boltzmann Machine doing logic program on a Hopfie1d network will be discussed. Besides that, global minima ratio of network programmed by program clauses in the features of running time and complexity are also been analyzed. Program experiments were conducted using Microsoft Basic C 6.0 software. The present study shows that the Boltzmann Machine model is more stable and efficient. Given a sufficient time, Boltzmann Machine is able to represent and solve difficult combinatory problems.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/3166
dc.subject	The Boltzmann Machine model is more stable and efficient	en_US
dc.subject	to represent and solve difficult combinatory problems.	en_US
dc.title	Implementation Of Boltzmann Machine In Neuro-Symbolic Integration	en_US
dc.type	Thesis	en_US

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