A new fuzzy back-propagation learning method based on derivation of min-max function tuned with genetic algorithms

Nowadays, environments are covered by lots of qualitative (inexact) data. Making proper decisions according to this qualitative data is an ultimate aim. Artificial neural networks can be adapted to the quantitative environments, since they have ability of learning. On the other hand, fuzzy logic has...

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Main Author:	Mashinchi, Mohammad Hadi
Format:	Thesis
Language:	English
Published:	2007
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/6796/1/MohammadHadiMashinchiMFSKSM2007.pdf
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id	my-utm-ep.6796
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spelling	my-utm-ep.67962018-08-30T08:03:51Z A new fuzzy back-propagation learning method based on derivation of min-max function tuned with genetic algorithms 2007-03 Mashinchi, Mohammad Hadi QA75 Electronic computers. Computer science Nowadays, environments are covered by lots of qualitative (inexact) data. Making proper decisions according to this qualitative data is an ultimate aim. Artificial neural networks can be adapted to the quantitative environments, since they have ability of learning. On the other hand, fuzzy logic has the power of dealing with inexact data. To get benefit of advantageous of artificial neural networks and fuzzy logic, fuzzy artificial neural networks are proposed. This hybrid soft-computing technique has the ability of learning in qualitative environments. Thus, fuzzy artificial neural networks can make qualitative decisions according to inexact data which are fed to it. Learning process of fuzzy artificial neural networks is one of the most important issues. Thus, many learning methods for feed forward fuzzy artificial neural networks are proposed. Low speed of convergence and accuracy of training have made fuzzy artificial methods inapplicable in most of problems. Thus, efficient learning method for fuzzy artificial neural networks is demandable. In this study a â€œgenetically tuned fuzzy back propagation method based on derivation of min-max functionâ€? as new learning method has been proposed by author. The proposed learning method has resolved some of the previous shortcomings. Importance of the proposed method is that, three main benefits are reached simultaneously; it can learn from any kind of convex fuzzy numbers, accuracy of training is higher since error function is more realistic comparing to gradient based learning methods, and convergence speed is acceptable. 2007-03 Thesis http://eprints.utm.my/id/eprint/6796/ http://eprints.utm.my/id/eprint/6796/1/MohammadHadiMashinchiMFSKSM2007.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62513 masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mashinchi, Mohammad Hadi A new fuzzy back-propagation learning method based on derivation of min-max function tuned with genetic algorithms
description	Nowadays, environments are covered by lots of qualitative (inexact) data. Making proper decisions according to this qualitative data is an ultimate aim. Artificial neural networks can be adapted to the quantitative environments, since they have ability of learning. On the other hand, fuzzy logic has the power of dealing with inexact data. To get benefit of advantageous of artificial neural networks and fuzzy logic, fuzzy artificial neural networks are proposed. This hybrid soft-computing technique has the ability of learning in qualitative environments. Thus, fuzzy artificial neural networks can make qualitative decisions according to inexact data which are fed to it. Learning process of fuzzy artificial neural networks is one of the most important issues. Thus, many learning methods for feed forward fuzzy artificial neural networks are proposed. Low speed of convergence and accuracy of training have made fuzzy artificial methods inapplicable in most of problems. Thus, efficient learning method for fuzzy artificial neural networks is demandable. In this study a â€œgenetically tuned fuzzy back propagation method based on derivation of min-max functionâ€? as new learning method has been proposed by author. The proposed learning method has resolved some of the previous shortcomings. Importance of the proposed method is that, three main benefits are reached simultaneously; it can learn from any kind of convex fuzzy numbers, accuracy of training is higher since error function is more realistic comparing to gradient based learning methods, and convergence speed is acceptable.
format	Thesis
qualification_level	Master's degree
author	Mashinchi, Mohammad Hadi
author_facet	Mashinchi, Mohammad Hadi
author_sort	Mashinchi, Mohammad Hadi
title	A new fuzzy back-propagation learning method based on derivation of min-max function tuned with genetic algorithms
title_short	A new fuzzy back-propagation learning method based on derivation of min-max function tuned with genetic algorithms
title_full	A new fuzzy back-propagation learning method based on derivation of min-max function tuned with genetic algorithms
title_fullStr	A new fuzzy back-propagation learning method based on derivation of min-max function tuned with genetic algorithms
title_full_unstemmed	A new fuzzy back-propagation learning method based on derivation of min-max function tuned with genetic algorithms
title_sort	new fuzzy back-propagation learning method based on derivation of min-max function tuned with genetic algorithms
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2007
url	http://eprints.utm.my/id/eprint/6796/1/MohammadHadiMashinchiMFSKSM2007.pdf
_version_	1747814688347914240

A new fuzzy back-propagation learning method based on derivation of min-max function tuned with genetic algorithms

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