Hybrid learning neuro-fuzzy approach for complex modeling using asymmetric fuzzy sets

Chunshien Li; Kuo Hsiang Cheng; Jiann Der Lee

doi:10.1109/ICTAI.2005.73

Hybrid learning neuro-fuzzy approach for complex modeling using asymmetric fuzzy sets

Chunshien Li, Kuo Hsiang Cheng, Jiann Der Lee

Department of Information Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

A hybrid learning neuro-fuzzy system with asymmetric fuzzy sets (HLNFS-A) is proposed in this paper. The learning methods of random optimization (RO) and least square estimation (LSE) are used in hybrid way to train the system parameters of HLNFS-A to achieve stable and fast convergence. In the HLNFS-A, the premise and the consequent parameters are updated by RO and LSE, respectively. With the proposed asymmetric fuzzy sets (AFS), the neuro-fuzzy system can capture the essence of nonlinear property of dynamic system, when used in the application of modeling. To demonstrate the feasibility and the potential of the proposed approach, an example of chaotic time series for system identification and prediction is given to verify the nonlinear mapping capability of the HLNFS-A. The experimental results show that the proposed HLNFS-A can achieve excellent performance for system modeling.

Original language	English
Title of host publication	ICTAI 2005
Subtitle of host publication	17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05
Publisher	IEEE Computer Society
Pages	5-9
Number of pages	5
ISBN (Print)	0769524885, 9780769524887
DOIs	https://doi.org/10.1109/ICTAI.2005.73
State	Published - 2005
Event	ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05 - Hong Kong, China Duration: 14 Nov 2005 → 16 Nov 2005

Publication series

Name	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume	2005
ISSN (Print)	1082-3409

Conference

Conference	ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05
Country/Territory	China
City	Hong Kong
Period	14/11/05 → 16/11/05

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10.1109/ICTAI.2005.73

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Li, C., Cheng, K. H., & Lee, J. D. (2005). Hybrid learning neuro-fuzzy approach for complex modeling using asymmetric fuzzy sets. In ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05 (pp. 5-9). [1562968] (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2005). IEEE Computer Society. https://doi.org/10.1109/ICTAI.2005.73

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title = "Hybrid learning neuro-fuzzy approach for complex modeling using asymmetric fuzzy sets",

abstract = "A hybrid learning neuro-fuzzy system with asymmetric fuzzy sets (HLNFS-A) is proposed in this paper. The learning methods of random optimization (RO) and least square estimation (LSE) are used in hybrid way to train the system parameters of HLNFS-A to achieve stable and fast convergence. In the HLNFS-A, the premise and the consequent parameters are updated by RO and LSE, respectively. With the proposed asymmetric fuzzy sets (AFS), the neuro-fuzzy system can capture the essence of nonlinear property of dynamic system, when used in the application of modeling. To demonstrate the feasibility and the potential of the proposed approach, an example of chaotic time series for system identification and prediction is given to verify the nonlinear mapping capability of the HLNFS-A. The experimental results show that the proposed HLNFS-A can achieve excellent performance for system modeling.",

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Li, C, Cheng, KH & Lee, JD 2005, Hybrid learning neuro-fuzzy approach for complex modeling using asymmetric fuzzy sets. in ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05., 1562968, Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, vol. 2005, IEEE Computer Society, pp. 5-9, ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05, Hong Kong, China, 14/11/05. https://doi.org/10.1109/ICTAI.2005.73

Hybrid learning neuro-fuzzy approach for complex modeling using asymmetric fuzzy sets. / Li, Chunshien; Cheng, Kuo Hsiang; Lee, Jiann Der.

ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05. IEEE Computer Society, 2005. p. 5-9 1562968 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - A hybrid learning neuro-fuzzy system with asymmetric fuzzy sets (HLNFS-A) is proposed in this paper. The learning methods of random optimization (RO) and least square estimation (LSE) are used in hybrid way to train the system parameters of HLNFS-A to achieve stable and fast convergence. In the HLNFS-A, the premise and the consequent parameters are updated by RO and LSE, respectively. With the proposed asymmetric fuzzy sets (AFS), the neuro-fuzzy system can capture the essence of nonlinear property of dynamic system, when used in the application of modeling. To demonstrate the feasibility and the potential of the proposed approach, an example of chaotic time series for system identification and prediction is given to verify the nonlinear mapping capability of the HLNFS-A. The experimental results show that the proposed HLNFS-A can achieve excellent performance for system modeling.

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Hybrid learning neuro-fuzzy approach for complex modeling using asymmetric fuzzy sets

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