A hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dynamic applications

Jeen-Shing Wang; Yen Ping Chen

doi:10.1109/FUZZY.2006.1681992

A hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dynamic applications

Jeen-Shing Wang
, Yen Ping Chen

Department of Electrical Engineering

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

1 Citation (Scopus)

Abstract

This paper presents a Hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dealing with dynamic applications. The proposed recurrent neuro-fuzzy system possesses two salient features: 1) it is capable of translating the complicated dynamic behavior of a system into a set of simple linguistic "dynamic" rules and into a state-space representation as well, and 2) with an automated hybrid construction algorithm, it can self-construct it network structure with a parsimonious size and satisfactory learning performance. Extensive computer simulations have been conducted to validate the effectiveness of the proposed approach for dynamic applications.

Original language	English
Title of host publication	2006 IEEE International Conference on Fuzzy Systems
Pages	2104-2111
Number of pages	8
DOIs	https://doi.org/10.1109/FUZZY.2006.1681992
Publication status	Published - 2006 Dec 1
Event	2006 IEEE International Conference on Fuzzy Systems - Vancouver, BC, Canada Duration: 2006 Jul 16 → 2006 Jul 21

Publication series

Name	IEEE International Conference on Fuzzy Systems
ISSN (Print)	1098-7584

Other

Other	2006 IEEE International Conference on Fuzzy Systems
Country/Territory	Canada
City	Vancouver, BC
Period	06-07-16 → 06-07-21

All Science Journal Classification (ASJC) codes

Software
Theoretical Computer Science
Artificial Intelligence
Applied Mathematics

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10.1109/FUZZY.2006.1681992

Cite this

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title = "A hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dynamic applications",

abstract = "This paper presents a Hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dealing with dynamic applications. The proposed recurrent neuro-fuzzy system possesses two salient features: 1) it is capable of translating the complicated dynamic behavior of a system into a set of simple linguistic {"}dynamic{"} rules and into a state-space representation as well, and 2) with an automated hybrid construction algorithm, it can self-construct it network structure with a parsimonious size and satisfactory learning performance. Extensive computer simulations have been conducted to validate the effectiveness of the proposed approach for dynamic applications.",

author = "Jeen-Shing Wang and Chen, \{Yen Ping\}",

year = "2006",

month = dec,

day = "1",

doi = "10.1109/FUZZY.2006.1681992",

language = "English",

isbn = "0780394887",

series = "IEEE International Conference on Fuzzy Systems",

pages = "2104--2111",

booktitle = "2006 IEEE International Conference on Fuzzy Systems",

note = "2006 IEEE International Conference on Fuzzy Systems ; Conference date: 16-07-2006 Through 21-07-2006",

}

Wang, J-S & Chen, YP 2006, A hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dynamic applications. in 2006 IEEE International Conference on Fuzzy Systems., 1681992, IEEE International Conference on Fuzzy Systems, pp. 2104-2111, 2006 IEEE International Conference on Fuzzy Systems, Vancouver, BC, Canada, 06-07-16. https://doi.org/10.1109/FUZZY.2006.1681992

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N2 - This paper presents a Hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dealing with dynamic applications. The proposed recurrent neuro-fuzzy system possesses two salient features: 1) it is capable of translating the complicated dynamic behavior of a system into a set of simple linguistic "dynamic" rules and into a state-space representation as well, and 2) with an automated hybrid construction algorithm, it can self-construct it network structure with a parsimonious size and satisfactory learning performance. Extensive computer simulations have been conducted to validate the effectiveness of the proposed approach for dynamic applications.

AB - This paper presents a Hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dealing with dynamic applications. The proposed recurrent neuro-fuzzy system possesses two salient features: 1) it is capable of translating the complicated dynamic behavior of a system into a set of simple linguistic "dynamic" rules and into a state-space representation as well, and 2) with an automated hybrid construction algorithm, it can self-construct it network structure with a parsimonious size and satisfactory learning performance. Extensive computer simulations have been conducted to validate the effectiveness of the proposed approach for dynamic applications.

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DO - 10.1109/FUZZY.2006.1681992

M3 - Conference contribution

AN - SCOPUS:34250705179

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BT - 2006 IEEE International Conference on Fuzzy Systems

T2 - 2006 IEEE International Conference on Fuzzy Systems

Y2 - 16 July 2006 through 21 July 2006

ER -

A hammerstein neuro-fuzzy network with an online hybrid construction algorithm for dynamic applications

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