Self-organizing neuro-fuzzy inference system

Héctor Allende-Cid; Alejandro Veloz; Rodrigo Salas; Steren Chabert; Héctor Allende

doi:10.1007/978-3-540-85920-8_53

Self-organizing neuro-fuzzy inference system

Héctor Allende-Cid, Alejandro Veloz, Rodrigo Salas, Steren Chabert, Héctor Allende

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

12 Scopus citations

Abstract

The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers's ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user's performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.

Original language	English
Title of host publication	Progress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings
Pages	429-436
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-540-85920-8_53
State	Published - 2008
Externally published	Yes
Event	13th Iberoamerican Congress on Pattern Recognition, CIARP 2008 - Havana, Cuba Duration: 9 Sep 2008 → 12 Sep 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5197 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Iberoamerican Congress on Pattern Recognition, CIARP 2008
Country/Territory	Cuba
City	Havana
Period	9/09/08 → 12/09/08

Keywords

ANFIS
Flexible architecture
Nonlinear modeling

Access to Document

10.1007/978-3-540-85920-8_53

Cite this

Allende-Cid, H., Veloz, A., Salas, R., Chabert, S., & Allende, H. (2008). Self-organizing neuro-fuzzy inference system. In Progress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings (pp. 429-436). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5197 LNCS). https://doi.org/10.1007/978-3-540-85920-8_53

Allende-Cid, Héctor ; Veloz, Alejandro ; Salas, Rodrigo et al. / Self-organizing neuro-fuzzy inference system. Progress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings. 2008. pp. 429-436 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers's ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user's performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.",

keywords = "ANFIS, Flexible architecture, Nonlinear modeling",

author = "H{\'e}ctor Allende-Cid and Alejandro Veloz and Rodrigo Salas and Steren Chabert and H{\'e}ctor Allende",

note = "Funding Information: This work was supported by the Fondecyt 1070220 and 11060036 research grants and DGIP-UTFSM grant.; 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008 ; Conference date: 09-09-2008 Through 12-09-2008",

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doi = "10.1007/978-3-540-85920-8_53",

language = "English",

isbn = "3540859195",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Allende-Cid, H, Veloz, A, Salas, R, Chabert, S & Allende, H 2008, Self-organizing neuro-fuzzy inference system. in Progress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5197 LNCS, pp. 429-436, 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Havana, Cuba, 9/09/08. https://doi.org/10.1007/978-3-540-85920-8_53

Self-organizing neuro-fuzzy inference system. / Allende-Cid, Héctor; Veloz, Alejandro; Salas, Rodrigo et al.
Progress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings. 2008. p. 429-436 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5197 LNCS).

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

TY - GEN

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AU - Allende-Cid, Héctor

AU - Veloz, Alejandro

AU - Salas, Rodrigo

AU - Chabert, Steren

AU - Allende, Héctor

N1 - Funding Information: This work was supported by the Fondecyt 1070220 and 11060036 research grants and DGIP-UTFSM grant.

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N2 - The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers's ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user's performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.

AB - The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers's ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user's performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.

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KW - Flexible architecture

KW - Nonlinear modeling

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Allende-Cid H, Veloz A, Salas R, Chabert S, Allende H. Self-organizing neuro-fuzzy inference system. In Progress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings. 2008. p. 429-436. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-85920-8_53

Self-organizing neuro-fuzzy inference system

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Keywords

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