Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength

Fuzzy Neural Networks (FNNs) with the integration of fuzzy logic, neural networks and optimization techniques have not only solved the issue of “black box” in Artificial Neural Networks (ANNs) but also have been effective in a wide variety of real-world applications. Despite of attracting researcher...

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Main Author:	Hussain Talpur, Kashif
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	QA76 Computer software
Online Access:	http://eprints.uthm.edu.my/1327/2/KASHIF%20HUSSAIN%20TALPUR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1327/1/24p%20KASHIF%20HUSSAIN%20TALPUR.pdf http://eprints.uthm.edu.my/1327/3/KASHIF%20HUSSAIN%20TALPUR%20WATERMARK.pdf
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spelling	my-uthm-ep.13272021-10-03T06:29:53Z Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength 2015-01 Hussain Talpur, Kashif QA76 Computer software Fuzzy Neural Networks (FNNs) with the integration of fuzzy logic, neural networks and optimization techniques have not only solved the issue of “black box” in Artificial Neural Networks (ANNs) but also have been effective in a wide variety of real-world applications. Despite of attracting researchers in recent years and outperforming other fuzzy inference systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rule-base optimization methods to perform efficiently when the number of inputs increase. Many researchers have trained ANFIS parameters using metaheuristic algorithms but very few have considered optimizing the ANFIS rule-base. Mine Blast Algorithm (MBA) which has been improved by Improved MBA (IMBA) can be further improved by modifying its exploitation phase. This research proposes Accelerated MBA (AMBA) to accelerate convergence of IMBA. The AMBA is then employed in proposed effective technique for optimizing ANFIS rule-base. The ANFIS optimized by AMBA is used employed to model classification of Malaysian small medium enterprises (SMEs) based on strength using non-financial factors. The performance of the proposed classification model is validated on SME dataset obtained from SME Corporation Malaysia, and also on real-world benchmark classification problems like Breast Cancer, Iris, and Glass. The performance of the ANFIS optimization by AMBA is compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), MBA and Improved MBA (IMBA), respectively. The results show that the proposed method achieved better accuracy with optimized rule-set in less number of iterations. 2015-01 Thesis http://eprints.uthm.edu.my/1327/ http://eprints.uthm.edu.my/1327/2/KASHIF%20HUSSAIN%20TALPUR%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1327/1/24p%20KASHIF%20HUSSAIN%20TALPUR.pdf text en public http://eprints.uthm.edu.my/1327/3/KASHIF%20HUSSAIN%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 Hussain Talpur, Kashif Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength
description	Fuzzy Neural Networks (FNNs) with the integration of fuzzy logic, neural networks and optimization techniques have not only solved the issue of “black box” in Artificial Neural Networks (ANNs) but also have been effective in a wide variety of real-world applications. Despite of attracting researchers in recent years and outperforming other fuzzy inference systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rule-base optimization methods to perform efficiently when the number of inputs increase. Many researchers have trained ANFIS parameters using metaheuristic algorithms but very few have considered optimizing the ANFIS rule-base. Mine Blast Algorithm (MBA) which has been improved by Improved MBA (IMBA) can be further improved by modifying its exploitation phase. This research proposes Accelerated MBA (AMBA) to accelerate convergence of IMBA. The AMBA is then employed in proposed effective technique for optimizing ANFIS rule-base. The ANFIS optimized by AMBA is used employed to model classification of Malaysian small medium enterprises (SMEs) based on strength using non-financial factors. The performance of the proposed classification model is validated on SME dataset obtained from SME Corporation Malaysia, and also on real-world benchmark classification problems like Breast Cancer, Iris, and Glass. The performance of the ANFIS optimization by AMBA is compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), MBA and Improved MBA (IMBA), respectively. The results show that the proposed method achieved better accuracy with optimized rule-set in less number of iterations.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Hussain Talpur, Kashif
author_facet	Hussain Talpur, Kashif
author_sort	Hussain Talpur, Kashif
title	Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength
title_short	Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength
title_full	Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength
title_fullStr	Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength
title_full_unstemmed	Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength
title_sort	improvement of fuzzy neural network using mine blast algorithm for classification of malaysian small medium enterprises based on strength
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2015
url	http://eprints.uthm.edu.my/1327/2/KASHIF%20HUSSAIN%20TALPUR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1327/1/24p%20KASHIF%20HUSSAIN%20TALPUR.pdf http://eprints.uthm.edu.my/1327/3/KASHIF%20HUSSAIN%20TALPUR%20WATERMARK.pdf
_version_	1747830771205275648

Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength

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