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Descripción
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| tThis paper presents an online TS fuzzy modeling general methodology based on the extended Kalmanfilter. The model can be obtained in a recursive way only based on input?output data. The methodologycan work online with the system, properly in the presence of noise, is very efficient computationally andcompletely general. It is general in the sense theorically there are no restrictions neither in the numberof inputs nor outputs, neither in the type nor distribution of membership functions used (which caneven be mixed in the antecedents of the rules). Some examples and comparisons with other online fuzzyidentification models from signals are provided to illustrate the skill of the online identification of theproposed methodology. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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APPLIED SOFT COMPUTING |
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ISSN
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1568-4946 |
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Factor de impacto JCR
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2,679 |
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Información de impacto
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Indice de Impacto: 2.679. Fuente: JCR2013. Area: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE. Posicion: 20/121 Area: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS. Posicion: 14/102 |
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Volumen
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13 |
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DOI
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10.1016/j.asoc.2013.07.026 |
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Número de revista
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12 |
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Desde la página
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4802 |
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Hasta la página
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4812 |
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Mes
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SIN MES |
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Ranking
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Indice de Impacto: 2.679. Fuente: JCR2013. Area: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE. Posicion: 20/121 Area: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS. Posicion: 14/102 |