Neuro-fuzzy learning of strategies for optimal control problems

Kaivan Kamali; Lijun Jiang; John Yen; K. W. Wang

doi:10.1109/NAFIPS.2005.1548533

Neuro-fuzzy learning of strategies for optimal control problems

Kaivan Kamali, Lijun Jiang, John Yen, K. W. Wang

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

Abstract

Various techniques have been proposed to automate the weight selection process in optimal control problems; yet these techniques do not provide symbolic rules that can be reused. We propose a layered approach for weight selection process in which Q-learning is used for selecting weighting matrices and hybrid genetic algorithm is used for selecting optimal design variables. Our approach can solve problems that genetic algorithm alone cannot solve. More importantly, the Q-learning's optimal policy enables the training of neuro-fuzzy systems which yields reusable knowledge in the form of fuzzy if-then rules. Experimental results show that the proposed method can automate the weight selection process and the fuzzy if-then rules acquired by training a neuro-fuzzy system can solve similar weight selection problems.

Original language	English (US)
Title of host publication	NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society
Pages	199-204
Number of pages	6
DOIs	https://doi.org/10.1109/NAFIPS.2005.1548533
State	Published - 2005
Event	NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society - Detroit, MI, United States Duration: Jun 26 2005 → Jun 28 2005

Publication series

Name	Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS
Volume	2005

Other

Other	NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society
Country/Territory	United States
City	Detroit, MI
Period	6/26/05 → 6/28/05

All Science Journal Classification (ASJC) codes

General Computer Science
General Mathematics

Access to Document

10.1109/NAFIPS.2005.1548533

Cite this

Kamali, K., Jiang, L., Yen, J., & Wang, K. W. (2005). Neuro-fuzzy learning of strategies for optimal control problems. In NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society (pp. 199-204). Article 1548533 (Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS; Vol. 2005). https://doi.org/10.1109/NAFIPS.2005.1548533

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title = "Neuro-fuzzy learning of strategies for optimal control problems",

abstract = "Various techniques have been proposed to automate the weight selection process in optimal control problems; yet these techniques do not provide symbolic rules that can be reused. We propose a layered approach for weight selection process in which Q-learning is used for selecting weighting matrices and hybrid genetic algorithm is used for selecting optimal design variables. Our approach can solve problems that genetic algorithm alone cannot solve. More importantly, the Q-learning's optimal policy enables the training of neuro-fuzzy systems which yields reusable knowledge in the form of fuzzy if-then rules. Experimental results show that the proposed method can automate the weight selection process and the fuzzy if-then rules acquired by training a neuro-fuzzy system can solve similar weight selection problems.",

author = "Kaivan Kamali and Lijun Jiang and John Yen and Wang, {K. W.}",

year = "2005",

doi = "10.1109/NAFIPS.2005.1548533",

language = "English (US)",

isbn = "078039187X",

series = "Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS",

pages = "199--204",

booktitle = "NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society",

note = "NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society ; Conference date: 26-06-2005 Through 28-06-2005",

}

Kamali, K, Jiang, L, Yen, J & Wang, KW 2005, Neuro-fuzzy learning of strategies for optimal control problems. in NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society., 1548533, Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, vol. 2005, pp. 199-204, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society, Detroit, MI, United States, 6/26/05. https://doi.org/10.1109/NAFIPS.2005.1548533

Neuro-fuzzy learning of strategies for optimal control problems. / Kamali, Kaivan; Jiang, Lijun; Yen, John et al.
NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society. 2005. p. 199-204 1548533 (Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS; Vol. 2005).

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

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AB - Various techniques have been proposed to automate the weight selection process in optimal control problems; yet these techniques do not provide symbolic rules that can be reused. We propose a layered approach for weight selection process in which Q-learning is used for selecting weighting matrices and hybrid genetic algorithm is used for selecting optimal design variables. Our approach can solve problems that genetic algorithm alone cannot solve. More importantly, the Q-learning's optimal policy enables the training of neuro-fuzzy systems which yields reusable knowledge in the form of fuzzy if-then rules. Experimental results show that the proposed method can automate the weight selection process and the fuzzy if-then rules acquired by training a neuro-fuzzy system can solve similar weight selection problems.

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ER -

Neuro-fuzzy learning of strategies for optimal control problems

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