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Robust optimization of ANFIS based on a new modified GA

Adaptive Network-based Fuzzy Inference Systems (ANFIS) is one of the most well-known predictions modeling technique utilized to find the superlative relationship between input and output parameters in different processes. Training the adaptive modeling parameters in ANFIS is still a challengeable pr...

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Main Authors: Sarkheyli, Arezoo, Mohd. Zain, Azlan, Sharif, Safian
Format: Article
Published: Elsevier 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/55347/
http://dx.doi.org/10.1016/j.neucom.2015.03.060
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spelling my.utm.553472016-09-04T02:01:09Z http://eprints.utm.my/id/eprint/55347/ Robust optimization of ANFIS based on a new modified GA Sarkheyli, Arezoo Mohd. Zain, Azlan Sharif, Safian QA75 Electronic computers. Computer science Adaptive Network-based Fuzzy Inference Systems (ANFIS) is one of the most well-known predictions modeling technique utilized to find the superlative relationship between input and output parameters in different processes. Training the adaptive modeling parameters in ANFIS is still a challengeable problem which has been recently considered by researchers. Hybridizing of a robust optimization algorithm with ANFIS as its training algorithm provides a scope to improve the effectiveness of membership functions and fuzzy rules in the model. In this paper, a new Modified Genetic Algorithm (MGA) by using a new type of population is proposed to optimize the modeling parameters for membership functions and fuzzy rules in ANFIS. As well, a case study on a machining process is considered to illustrate the robustness of the proposed training technique in prediction of machining performances. The prediction results have demonstrated the superiority of the presented hybrid ANFIS-MGA in term of prediction accuracy (with 97.74%) over the other techniques such as hybridization of ANFIS with Genetic Algorithm (GA), Taguchi-GA, Hybrid Learning algorithm (HL), Leave-One-Out Cross-Validation (LOO-CV), Particle Swarm Optimization (PSO) and Grid Partition method (GP), as well as RBFN and basic Grid Partition Method (GPM). In addition, an attempt is done to specify the effectiveness of different improvement rates on the prediction result and measuring the number of function evaluations required. The comparison result reveals that MGA with improvement rate 0.8 raises the convergence speed and accuracy of the prediction results compared to GA. Elsevier 2015-10-20 Article PeerReviewed Sarkheyli, Arezoo and Mohd. Zain, Azlan and Sharif, Safian (2015) Robust optimization of ANFIS based on a new modified GA. Neurocomputing, 166 . pp. 357-366. ISSN 0925-2312 http://dx.doi.org/10.1016/j.neucom.2015.03.060 DOI:10.1016/j.neucom.2015.03.060
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Sarkheyli, Arezoo
Mohd. Zain, Azlan
Sharif, Safian
Robust optimization of ANFIS based on a new modified GA
description Adaptive Network-based Fuzzy Inference Systems (ANFIS) is one of the most well-known predictions modeling technique utilized to find the superlative relationship between input and output parameters in different processes. Training the adaptive modeling parameters in ANFIS is still a challengeable problem which has been recently considered by researchers. Hybridizing of a robust optimization algorithm with ANFIS as its training algorithm provides a scope to improve the effectiveness of membership functions and fuzzy rules in the model. In this paper, a new Modified Genetic Algorithm (MGA) by using a new type of population is proposed to optimize the modeling parameters for membership functions and fuzzy rules in ANFIS. As well, a case study on a machining process is considered to illustrate the robustness of the proposed training technique in prediction of machining performances. The prediction results have demonstrated the superiority of the presented hybrid ANFIS-MGA in term of prediction accuracy (with 97.74%) over the other techniques such as hybridization of ANFIS with Genetic Algorithm (GA), Taguchi-GA, Hybrid Learning algorithm (HL), Leave-One-Out Cross-Validation (LOO-CV), Particle Swarm Optimization (PSO) and Grid Partition method (GP), as well as RBFN and basic Grid Partition Method (GPM). In addition, an attempt is done to specify the effectiveness of different improvement rates on the prediction result and measuring the number of function evaluations required. The comparison result reveals that MGA with improvement rate 0.8 raises the convergence speed and accuracy of the prediction results compared to GA.
format Article
author Sarkheyli, Arezoo
Mohd. Zain, Azlan
Sharif, Safian
author_facet Sarkheyli, Arezoo
Mohd. Zain, Azlan
Sharif, Safian
author_sort Sarkheyli, Arezoo
title Robust optimization of ANFIS based on a new modified GA
title_short Robust optimization of ANFIS based on a new modified GA
title_full Robust optimization of ANFIS based on a new modified GA
title_fullStr Robust optimization of ANFIS based on a new modified GA
title_full_unstemmed Robust optimization of ANFIS based on a new modified GA
title_sort robust optimization of anfis based on a new modified ga
publisher Elsevier
publishDate 2015
url http://eprints.utm.my/id/eprint/55347/
http://dx.doi.org/10.1016/j.neucom.2015.03.060
_version_ 1643653770528161792
score 13.211869

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