Fault detection and isolation using a Neo fuzzy neuron-based system

D. Novoa-Paredes; F. Rivas-Echeverría; C. Bravo-Bravo

doi:10.1117/12.421161

Fault detection and isolation using a Neo fuzzy neuron-based system

D. Novoa-Paredes^*
, F. Rivas-Echeverría
, C. Bravo-Bravo

^*Autor correspondiente de este trabajo

Producción científica: Revista › Artículo de la conferencia › revisión exhaustiva

Resumen

In this paper a fault detection and isolation scheme using a set of Neo fuzzy neurons will be presented. Such neurons use IFTHEN rules for characterizing the synaptic junctions in order to obtain complex nonlinear input/output maps in a simple structure, allowing an improvement of the learning and representation capabilities. As illustrative example, the fault detection scheme in a three interconnected tank system will be presented.

Idioma original	Inglés
Páginas (desde-hasta)	125-133
Número de páginas	9
Publicación	Proceedings of SPIE - The International Society for Optical Engineering
Volumen	4390
DOI	https://doi.org/10.1117/12.421161
Estado	Publicada - 2001
Publicado de forma externa	Sí
Evento	Applications and Science of Computational Intelligence IV - Orlando, FL, Estados Unidos Duración: 17 abr 2001 → 18 abr 2001

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10.1117/12.421161

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title = "Fault detection and isolation using a Neo fuzzy neuron-based system",

abstract = "In this paper a fault detection and isolation scheme using a set of Neo fuzzy neurons will be presented. Such neurons use IFTHEN rules for characterizing the synaptic junctions in order to obtain complex nonlinear input/output maps in a simple structure, allowing an improvement of the learning and representation capabilities. As illustrative example, the fault detection scheme in a three interconnected tank system will be presented.",

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author = "D. Novoa-Paredes and F. Rivas-Echeverr{\'i}a and C. Bravo-Bravo",

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AU - Rivas-Echeverría, F.

AU - Bravo-Bravo, C.

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AB - In this paper a fault detection and isolation scheme using a set of Neo fuzzy neurons will be presented. Such neurons use IFTHEN rules for characterizing the synaptic junctions in order to obtain complex nonlinear input/output maps in a simple structure, allowing an improvement of the learning and representation capabilities. As illustrative example, the fault detection scheme in a three interconnected tank system will be presented.

KW - Fault detection

KW - Fuzzy logic

KW - Neo-fuzzy neurons

KW - Neural networks

UR - https://www.scopus.com/pages/publications/0034942926

U2 - 10.1117/12.421161

DO - 10.1117/12.421161

M3 - Artículo de la conferencia

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JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Applications and Science of Computational Intelligence IV

Y2 - 17 April 2001 through 18 April 2001

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