A new on-line self-constructing neural fuzzy network

Andrés Ferreyra; José De Jesús Rubio

doi:10.1109/cdc.2006.377770

A new on-line self-constructing neural fuzzy network

Andrés Ferreyra, José De Jesús Rubio

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

7 Scopus citations

Abstract

In this paper, we propose a new on-line self-constructing neural fuzzy network. Structure and parameter learning are updated at the same time in our algorithm, because there is no difference between them. It generates groups with a given radius. The center is updated in order to get a nearest one to the incoming data in each iteration, in this way, it does not generate many rules and it does not need to prune them. We give a time varying learning rate for backpropagation training. We use extended Kalman filter to train the center of sets in the THEN part. We proved the stability in both cases.

Original language	English
Title of host publication	Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3003-3009
Number of pages	7
ISBN (Print)	1424401712, 9781424401710
DOIs	https://doi.org/10.1109/cdc.2006.377770
State	Published - 2006
Externally published	Yes
Event	45th IEEE Conference on Decision and Control 2006, CDC - San Diego, CA, United States Duration: 13 Dec 2006 → 15 Dec 2006

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	45th IEEE Conference on Decision and Control 2006, CDC
Country/Territory	United States
City	San Diego, CA
Period	13/12/06 → 15/12/06

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10.1109/cdc.2006.377770

Cite this

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title = "A new on-line self-constructing neural fuzzy network",

abstract = "In this paper, we propose a new on-line self-constructing neural fuzzy network. Structure and parameter learning are updated at the same time in our algorithm, because there is no difference between them. It generates groups with a given radius. The center is updated in order to get a nearest one to the incoming data in each iteration, in this way, it does not generate many rules and it does not need to prune them. We give a time varying learning rate for backpropagation training. We use extended Kalman filter to train the center of sets in the THEN part. We proved the stability in both cases.",

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year = "2006",

doi = "10.1109/cdc.2006.377770",

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

isbn = "1424401712",

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note = "45th IEEE Conference on Decision and Control 2006, CDC ; Conference date: 13-12-2006 Through 15-12-2006",

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Ferreyra, A & De Jesús Rubio, J 2006, A new on-line self-constructing neural fuzzy network. in Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC., 4177880, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 3003-3009, 45th IEEE Conference on Decision and Control 2006, CDC, San Diego, CA, United States, 13/12/06. https://doi.org/10.1109/cdc.2006.377770

A new on-line self-constructing neural fuzzy network. / Ferreyra, Andrés; De Jesús Rubio, José.
Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC. Institute of Electrical and Electronics Engineers Inc., 2006. p. 3003-3009 4177880 (Proceedings of the IEEE Conference on Decision and Control).

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

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AB - In this paper, we propose a new on-line self-constructing neural fuzzy network. Structure and parameter learning are updated at the same time in our algorithm, because there is no difference between them. It generates groups with a given radius. The center is updated in order to get a nearest one to the incoming data in each iteration, in this way, it does not generate many rules and it does not need to prune them. We give a time varying learning rate for backpropagation training. We use extended Kalman filter to train the center of sets in the THEN part. We proved the stability in both cases.

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A new on-line self-constructing neural fuzzy network

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