Modified anfis architecture with less computational complexities for classification problems

Adaptive Neuro Fuzzy Inference System (ANFIS) is one of those soft computing techniques that have solved the problems effectively in a wide variety of real-world applications. Even though it has been widely used, ANFIS architecture still has a drawback of computational complexities. The number of ru...

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Main Author:	Talpur, Noureen
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	QA76 Computer software
Online Access:	http://eprints.uthm.edu.my/275/1/24p%20NOUREEN%20TALPUR.pdf http://eprints.uthm.edu.my/275/2/NOUREEN%20TALPUR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/275/3/NOUREEN%20TALPUR%20WATERMARK.pdf
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spelling	my-uthm-ep.2752021-07-21T02:09:52Z Modified anfis architecture with less computational complexities for classification problems 2018-07 Talpur, Noureen QA76 Computer software Adaptive Neuro Fuzzy Inference System (ANFIS) is one of those soft computing techniques that have solved the problems effectively in a wide variety of real-world applications. Even though it has been widely used, ANFIS architecture still has a drawback of computational complexities. The number of rules and its tunable parameters increase exponentially which created the problem of curse of dimensionality. Moreover, the standard architecture has a key drawback because of using grid partitioning and combination of gradient descent (GD) and least square estimation (LSE) which have problem to be likely trapped in local minima. Even though grid partitioning method is very useful to generate better accuracy for ANFIS model, since it generates maximum number of rules by considering all possibilities, but it also increases computational complexity. Since, ANFIS use fuzzy logic, the model accuracy is highly dependent on selecting the appropriate type of membership function. Furthermore, researchers have mainly used metaheuristic algorithms to avoid the problem of local minima in standard learning method. In this study, the experiments have been made to find out best suitable membership function for ANFIS model. Additionally, ANFIS architecture is modified for lessening computational complexities of the ANFIS architecture by reducing the fourth layer and reducing the trainable parameters as well. The proposed ANFIS model is trained by one of the metaheuristics approach instead of standard two pass learning algorithm. The performance of proposed modified ANFIS architecture is validated with the standard ANFIS architecture for solving classification problems. The results show that the proposed modified ANFIS architecture with gaussian membership function and Artificial Bee Colony (ABC) optimization algorithm, on average has achieved classification accuracy of 99.5% with 83% less computational complexity. 2018-07 Thesis http://eprints.uthm.edu.my/275/ http://eprints.uthm.edu.my/275/1/24p%20NOUREEN%20TALPUR.pdf text en public http://eprints.uthm.edu.my/275/2/NOUREEN%20TALPUR%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/275/3/NOUREEN%20TALPUR%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Talpur, Noureen Modified anfis architecture with less computational complexities for classification problems
description	Adaptive Neuro Fuzzy Inference System (ANFIS) is one of those soft computing techniques that have solved the problems effectively in a wide variety of real-world applications. Even though it has been widely used, ANFIS architecture still has a drawback of computational complexities. The number of rules and its tunable parameters increase exponentially which created the problem of curse of dimensionality. Moreover, the standard architecture has a key drawback because of using grid partitioning and combination of gradient descent (GD) and least square estimation (LSE) which have problem to be likely trapped in local minima. Even though grid partitioning method is very useful to generate better accuracy for ANFIS model, since it generates maximum number of rules by considering all possibilities, but it also increases computational complexity. Since, ANFIS use fuzzy logic, the model accuracy is highly dependent on selecting the appropriate type of membership function. Furthermore, researchers have mainly used metaheuristic algorithms to avoid the problem of local minima in standard learning method. In this study, the experiments have been made to find out best suitable membership function for ANFIS model. Additionally, ANFIS architecture is modified for lessening computational complexities of the ANFIS architecture by reducing the fourth layer and reducing the trainable parameters as well. The proposed ANFIS model is trained by one of the metaheuristics approach instead of standard two pass learning algorithm. The performance of proposed modified ANFIS architecture is validated with the standard ANFIS architecture for solving classification problems. The results show that the proposed modified ANFIS architecture with gaussian membership function and Artificial Bee Colony (ABC) optimization algorithm, on average has achieved classification accuracy of 99.5% with 83% less computational complexity.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Talpur, Noureen
author_facet	Talpur, Noureen
author_sort	Talpur, Noureen
title	Modified anfis architecture with less computational complexities for classification problems
title_short	Modified anfis architecture with less computational complexities for classification problems
title_full	Modified anfis architecture with less computational complexities for classification problems
title_fullStr	Modified anfis architecture with less computational complexities for classification problems
title_full_unstemmed	Modified anfis architecture with less computational complexities for classification problems
title_sort	modified anfis architecture with less computational complexities for classification problems
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2018
url	http://eprints.uthm.edu.my/275/1/24p%20NOUREEN%20TALPUR.pdf http://eprints.uthm.edu.my/275/2/NOUREEN%20TALPUR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/275/3/NOUREEN%20TALPUR%20WATERMARK.pdf
_version_	1747830570750050304

Modified anfis architecture with less computational complexities for classification problems

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