Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods

Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods

In this paper, ad hoc and system identification methods are used to generate fuzzy If-Then rules for a zeroorder Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) using a set of multi-attribute monotone data. Convex and normal trapezoidal fuzzy sets, with a strong fuzzy partition strategy,...

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Main Authors: Chin, Ying Teh, Tay, Kai Meng, Chee, Peng Lim
Format: Article
Language:English
Published: IEEE 2017
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://ir.unimas.my/17423/
http://ir.unimas.my/17423/
http://ir.unimas.my/17423/
http://ir.unimas.my/17423/1/Monotone%20Data%20Samples%20Do%20Not%20Always%20Produce%20Monotone%20Fuzzy%20%28abstract%29.pdf
id unimas-17423
recordtype eprints
spelling unimas-174232017-08-28T07:17:27Z http://ir.unimas.my/17423/ Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods Chin, Ying Teh Tay, Kai Meng Chee, Peng Lim TK Electrical engineering. Electronics Nuclear engineering In this paper, ad hoc and system identification methods are used to generate fuzzy If-Then rules for a zeroorder Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) using a set of multi-attribute monotone data. Convex and normal trapezoidal fuzzy sets, with a strong fuzzy partition strategy, is employed. Our analysis shows that even with multi-attribute monotone data, non-monotone fuzzy If- Then rules can be produced using an ad hoc method. The same observation can be made, empirically, using a system identification method, e.g., a derivative–based optimization method and the genetic algorithm. This finding is important for modeling a monotone FIS model, as the result shows that even with a “clean” data set pertaining to a monotone system, the generated fuzzy If-Then rules may need to be preprocessed, before being used for FIS modeling. As such, monotone fuzzy rule relabeling is useful. Besides that, a constrained non-linear programming method for FIS modelling is suggested, as a variant of the system identification method. IEEE 2017 Article PeerReviewed text en http://ir.unimas.my/17423/1/Monotone%20Data%20Samples%20Do%20Not%20Always%20Produce%20Monotone%20Fuzzy%20%28abstract%29.pdf Chin, Ying Teh and Tay, Kai Meng and Chee, Peng Lim (2017) Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017. ISSN 1558-4739 http://ieeexplore.ieee.org/document/8015386/ DOI: 10.1109/FUZZ-IEEE.2017.8015386
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Sarawak
building UNIMAS Institutional Repository
collection Online Access
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Chin, Ying Teh
Tay, Kai Meng
Chee, Peng Lim
Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods
description In this paper, ad hoc and system identification methods are used to generate fuzzy If-Then rules for a zeroorder Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) using a set of multi-attribute monotone data. Convex and normal trapezoidal fuzzy sets, with a strong fuzzy partition strategy, is employed. Our analysis shows that even with multi-attribute monotone data, non-monotone fuzzy If- Then rules can be produced using an ad hoc method. The same observation can be made, empirically, using a system identification method, e.g., a derivative–based optimization method and the genetic algorithm. This finding is important for modeling a monotone FIS model, as the result shows that even with a “clean” data set pertaining to a monotone system, the generated fuzzy If-Then rules may need to be preprocessed, before being used for FIS modeling. As such, monotone fuzzy rule relabeling is useful. Besides that, a constrained non-linear programming method for FIS modelling is suggested, as a variant of the system identification method.
format Article
author Chin, Ying Teh
Tay, Kai Meng
Chee, Peng Lim
author_facet Chin, Ying Teh
Tay, Kai Meng
Chee, Peng Lim
author_sort Chin, Ying Teh
title Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods
title_short Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods
title_full Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods
title_fullStr Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods
title_full_unstemmed Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods
title_sort monotone data samples do not always produce monotone fuzzy if- then rules: learning with ad hoc and system identification methods
publisher IEEE
publishDate 2017
url http://ir.unimas.my/17423/
http://ir.unimas.my/17423/
http://ir.unimas.my/17423/
http://ir.unimas.my/17423/1/Monotone%20Data%20Samples%20Do%20Not%20Always%20Produce%20Monotone%20Fuzzy%20%28abstract%29.pdf
first_indexed 2018-09-06T16:36:23Z
last_indexed 2018-09-06T16:36:23Z
_version_ 1610876749951795200

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