Nonlinear systems identification via two types of recurrent fuzzy CMAC

Floriberto Ortiz Rodriguez; Wen Yu; Marco A. Moreno-Armendariz

doi:10.1109/IJCNN.2007.4371064

Nonlinear systems identification via two types of recurrent fuzzy CMAC

Floriberto Ortiz Rodriguez, Wen Yu, Marco A. Moreno-Armendariz

Centro de Investigación en Computación (CIC)

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

Abstract

Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs, and it is difficult for its static structure to model a dynamic system. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms are presented that have time-varying learning rates, the stabilities of the neural identifications are proven.

Original language	English
Title of host publication	The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages	823-828
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2007.4371064
State	Published - 2007
Event	2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States Duration: 12 Aug 2007 → 17 Aug 2007

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)	1098-7576

Conference

Conference	2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/Territory	United States
City	Orlando, FL
Period	12/08/07 → 17/08/07

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10.1109/IJCNN.2007.4371064

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Rodriguez, F. O., Yu, W., & Moreno-Armendariz, M. A. (2007). Nonlinear systems identification via two types of recurrent fuzzy CMAC. In The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings (pp. 823-828). Article 4371064 (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/IJCNN.2007.4371064

@inproceedings{ff29106f25e44cce82bfadc8f3a74377,

title = "Nonlinear systems identification via two types of recurrent fuzzy CMAC",

abstract = "Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs, and it is difficult for its static structure to model a dynamic system. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms are presented that have time-varying learning rates, the stabilities of the neural identifications are proven.",

author = "Rodriguez, {Floriberto Ortiz} and Wen Yu and Moreno-Armendariz, {Marco A.}",

year = "2007",

doi = "10.1109/IJCNN.2007.4371064",

language = "Ingl{\'e}s",

isbn = "142441380X",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

pages = "823--828",

booktitle = "The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings",

note = "2007 International Joint Conference on Neural Networks, IJCNN 2007 ; Conference date: 12-08-2007 Through 17-08-2007",

}

Rodriguez, FO, Yu, W & Moreno-Armendariz, MA 2007, Nonlinear systems identification via two types of recurrent fuzzy CMAC. in The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings., 4371064, IEEE International Conference on Neural Networks - Conference Proceedings, pp. 823-828, 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, United States, 12/08/07. https://doi.org/10.1109/IJCNN.2007.4371064

Nonlinear systems identification via two types of recurrent fuzzy CMAC. / Rodriguez, Floriberto Ortiz; Yu, Wen; Moreno-Armendariz, Marco A.
The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings. 2007. p. 823-828 4371064 (IEEE International Conference on Neural Networks - Conference Proceedings).

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

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AU - Yu, Wen

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N2 - Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs, and it is difficult for its static structure to model a dynamic system. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms are presented that have time-varying learning rates, the stabilities of the neural identifications are proven.

AB - Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs, and it is difficult for its static structure to model a dynamic system. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms are presented that have time-varying learning rates, the stabilities of the neural identifications are proven.

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M3 - Contribución a la conferencia

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SN - 9781424413805

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BT - The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings

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Y2 - 12 August 2007 through 17 August 2007

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Nonlinear systems identification via two types of recurrent fuzzy CMAC

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