Developing Agent Based Modeling For Logic Programming And Reverse Analysis For Hopfield Network

For higher-order programming, higher order network architecture is necessary as high order neural networks have faster convergence rate, greater storage capacity, stronger approximation property, and higher fault tolerance than lower-order neural networks. So, higher order Hopfield network is brough...

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Main Author:	Ng, Pei Fen
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QD1-999 Chemistry
Online Access:	http://eprints.usm.my/43864/1/Ng%20Pei%20Fen24.pdf
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spelling	my-usm-ep.438642019-04-12T05:26:14Z Developing Agent Based Modeling For Logic Programming And Reverse Analysis For Hopfield Network 2013-06 Ng, Pei Fen QD1-999 Chemistry For higher-order programming, higher order network architecture is necessary as high order neural networks have faster convergence rate, greater storage capacity, stronger approximation property, and higher fault tolerance than lower-order neural networks. So, higher order Hopfield network is brought into this thesis by using logic programming and reverse analysis in Hopfield network. The goal of performing 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. Thus, Boltzmann Machines and Hyperbolic Tangent activation function are being introduced to overcome this problem. To choose the best and efficient method to obtain the global minima among Wan Abdullah method (use McCulloch-Pitts updating rule in Hopfield net), Boltzmann machine and Hyperbolic Tangent activation functions, a comparison table will be created in this thesis. To carry out such work, agent based modeling (ABM) is created. NetLogo as the platform to carry out logic programming and reverse analysis. ABM can allow rapid development of models, easy addition of features and a user friendly handling and coding. In logic programming systems, not only the result in terms of global minimum will be analyzed but in the aspect of hamming distance and central processing unit (CPU) times will also be carried out. In reverse analysis systems, the inherent relationships among the data can be learned by extracting common patterns that exist in data sets. The unknown and unexpected relation can be seek. As a result, real life cases will be carried out by using ABM to run computer simulation in this thesis. 2013-06 Thesis http://eprints.usm.my/43864/ http://eprints.usm.my/43864/1/Ng%20Pei%20Fen24.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Sains Kimia
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QD1-999 Chemistry
spellingShingle	QD1-999 Chemistry Ng, Pei Fen Developing Agent Based Modeling For Logic Programming And Reverse Analysis For Hopfield Network
description	For higher-order programming, higher order network architecture is necessary as high order neural networks have faster convergence rate, greater storage capacity, stronger approximation property, and higher fault tolerance than lower-order neural networks. So, higher order Hopfield network is brought into this thesis by using logic programming and reverse analysis in Hopfield network. The goal of performing 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. Thus, Boltzmann Machines and Hyperbolic Tangent activation function are being introduced to overcome this problem. To choose the best and efficient method to obtain the global minima among Wan Abdullah method (use McCulloch-Pitts updating rule in Hopfield net), Boltzmann machine and Hyperbolic Tangent activation functions, a comparison table will be created in this thesis. To carry out such work, agent based modeling (ABM) is created. NetLogo as the platform to carry out logic programming and reverse analysis. ABM can allow rapid development of models, easy addition of features and a user friendly handling and coding. In logic programming systems, not only the result in terms of global minimum will be analyzed but in the aspect of hamming distance and central processing unit (CPU) times will also be carried out. In reverse analysis systems, the inherent relationships among the data can be learned by extracting common patterns that exist in data sets. The unknown and unexpected relation can be seek. As a result, real life cases will be carried out by using ABM to run computer simulation in this thesis.
format	Thesis
qualification_level	Master's degree
author	Ng, Pei Fen
author_facet	Ng, Pei Fen
author_sort	Ng, Pei Fen
title	Developing Agent Based Modeling For Logic Programming And Reverse Analysis For Hopfield Network
title_short	Developing Agent Based Modeling For Logic Programming And Reverse Analysis For Hopfield Network
title_full	Developing Agent Based Modeling For Logic Programming And Reverse Analysis For Hopfield Network
title_fullStr	Developing Agent Based Modeling For Logic Programming And Reverse Analysis For Hopfield Network
title_full_unstemmed	Developing Agent Based Modeling For Logic Programming And Reverse Analysis For Hopfield Network
title_sort	developing agent based modeling for logic programming and reverse analysis for hopfield network
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Kimia
publishDate	2013
url	http://eprints.usm.my/43864/1/Ng%20Pei%20Fen24.pdf
_version_	1747821292265930752

Developing Agent Based Modeling For Logic Programming And Reverse Analysis For Hopfield Network

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