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New Evolving Fuzzy System Algorithms Using Dynamic Constraint

An information granule has to be translated into significant frameworks of granular computing to realize interpretability-accuracy tradeoff. These two objectives are in conflict and constitute an open problem. Evolving information granules is a significant concept of granular computing which conside...

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Main Author: Ahme, Md. Manjur
Format: Thesis
Language:English
Published: 2016
Subjects:
T Technology
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Online Access:http://eprints.usm.my/46911/1/New%20Evolving%20Fuzzy%20System%20Algorithms%20Using%20Dynamic%20Constraint.pdf
http://eprints.usm.my/46911/
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spelling my.usm.eprints.46911 http://eprints.usm.my/46911/ New Evolving Fuzzy System Algorithms Using Dynamic Constraint Ahme, Md. Manjur T Technology TK1-9971 Electrical engineering. Electronics. Nuclear engineering An information granule has to be translated into significant frameworks of granular computing to realize interpretability-accuracy tradeoff. These two objectives are in conflict and constitute an open problem. Evolving information granules is a significant concept of granular computing which consider coarser partition (or lower granule i.e. higher error) to fine partition (or higher granule i.e. lower error). While this error reducing granular framework is considered, interpretability constraint is the factor to improve the tradeoff between interpretability and accuracy. Furthermore, overfitting and underfitting criteria are noteworthy to be considered while evolving process continues. In addition, the stability-plasticity tradeoff is another significant consideration to design a granular framework in order to find a consistent and up-to-date fuzzy information granule method. A new operational framework namely evolving fuzzy system (EFS) is developed in this research work, which ensures a compromise between interpretability and reasonable accuracy. Three models are designed based on EFS namely, evolving structural fuzzy system (ESFS), evolving output-context fuzzy system (EOCFS) and evolving information granule (EIG). The evolving information granule is initiated with the first information granule by translating the knowledge of the entire output domain. The initial information granule is considered as an underfitting state with a high approximation error. Then, the EFS starts evolving in the information granule by partitioning the output (or input) domain and uses a dynamic constraint to maintain semantic interpretability in the output (or input) contexts. The outcome on the synthetic and real-world data using the EFS shows the effectiveness of the proposed system, which outperforms state-of-the-art methods. The EFS needs less number of rules (i.e. high interpretable) and low error (i.e. high accuracy) with respect to the existing methods. For example, if the proposed EIG method is applied to the Nakanishi‘s nonlinear system then four fuzzy rules and 0.142 mean square error (MSE) are found. Furthermore, the EIG outperforms if compared with the existing methods. The important criterion in the EFS is to determine the prominent distinction (output or input context) and realize the distinct information granule that depicts the semantics at the fuzzy partition level. The EFS tends to evolve toward the lower error region and realizes the effective rule base by avoiding overfitting. Furthermore, the evolving overfitting index and uncertainty controller of the self-adaptive process are dynamically attained from past and current knowledge. Therefore, effective rule base is the balanced fuzzy model of the approximated system. Within the proposed three models (ESFS, EOCFS and EIG), EIG has the significant ability to tradeoff between interpretability and accuracy, while the proposed ESFS method shows the highly interpretable granular framework which also realizes the interpretability-accuracy tradeoff. 2016-01-01 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/46911/1/New%20Evolving%20Fuzzy%20System%20Algorithms%20Using%20Dynamic%20Constraint.pdf Ahme, Md. Manjur (2016) New Evolving Fuzzy System Algorithms Using Dynamic Constraint. PhD thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
spellingShingle T Technology
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Ahme, Md. Manjur
New Evolving Fuzzy System Algorithms Using Dynamic Constraint
description An information granule has to be translated into significant frameworks of granular computing to realize interpretability-accuracy tradeoff. These two objectives are in conflict and constitute an open problem. Evolving information granules is a significant concept of granular computing which consider coarser partition (or lower granule i.e. higher error) to fine partition (or higher granule i.e. lower error). While this error reducing granular framework is considered, interpretability constraint is the factor to improve the tradeoff between interpretability and accuracy. Furthermore, overfitting and underfitting criteria are noteworthy to be considered while evolving process continues. In addition, the stability-plasticity tradeoff is another significant consideration to design a granular framework in order to find a consistent and up-to-date fuzzy information granule method. A new operational framework namely evolving fuzzy system (EFS) is developed in this research work, which ensures a compromise between interpretability and reasonable accuracy. Three models are designed based on EFS namely, evolving structural fuzzy system (ESFS), evolving output-context fuzzy system (EOCFS) and evolving information granule (EIG). The evolving information granule is initiated with the first information granule by translating the knowledge of the entire output domain. The initial information granule is considered as an underfitting state with a high approximation error. Then, the EFS starts evolving in the information granule by partitioning the output (or input) domain and uses a dynamic constraint to maintain semantic interpretability in the output (or input) contexts. The outcome on the synthetic and real-world data using the EFS shows the effectiveness of the proposed system, which outperforms state-of-the-art methods. The EFS needs less number of rules (i.e. high interpretable) and low error (i.e. high accuracy) with respect to the existing methods. For example, if the proposed EIG method is applied to the Nakanishi‘s nonlinear system then four fuzzy rules and 0.142 mean square error (MSE) are found. Furthermore, the EIG outperforms if compared with the existing methods. The important criterion in the EFS is to determine the prominent distinction (output or input context) and realize the distinct information granule that depicts the semantics at the fuzzy partition level. The EFS tends to evolve toward the lower error region and realizes the effective rule base by avoiding overfitting. Furthermore, the evolving overfitting index and uncertainty controller of the self-adaptive process are dynamically attained from past and current knowledge. Therefore, effective rule base is the balanced fuzzy model of the approximated system. Within the proposed three models (ESFS, EOCFS and EIG), EIG has the significant ability to tradeoff between interpretability and accuracy, while the proposed ESFS method shows the highly interpretable granular framework which also realizes the interpretability-accuracy tradeoff.
format Thesis
author Ahme, Md. Manjur
author_facet Ahme, Md. Manjur
author_sort Ahme, Md. Manjur
title New Evolving Fuzzy System Algorithms Using Dynamic Constraint
title_short New Evolving Fuzzy System Algorithms Using Dynamic Constraint
title_full New Evolving Fuzzy System Algorithms Using Dynamic Constraint
title_fullStr New Evolving Fuzzy System Algorithms Using Dynamic Constraint
title_full_unstemmed New Evolving Fuzzy System Algorithms Using Dynamic Constraint
title_sort new evolving fuzzy system algorithms using dynamic constraint
publishDate 2016
url http://eprints.usm.my/46911/1/New%20Evolving%20Fuzzy%20System%20Algorithms%20Using%20Dynamic%20Constraint.pdf
http://eprints.usm.my/46911/
_version_ 1717094482044780544
score 13.211869

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