Diseño de Sistemas de Control Borroso: Modelado de la Planta
[EN] In this report, the authors present a methodology that allows the fuzzy modeling as state equations of a nonlinear multivariable control system. The mathematical formalization of the fuzzy control system is totally developed, without any restrictions, neither in state vector of the plant nor in...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2010 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/146405 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/146405 |
| Access Level: | acceso abierto |
| Palabra clave: | Identification Ant colonies Modeling Neurofuzzy Identificación Colonia de hormigas Modelado Neuroborroso |
| Sumario: | [EN] In this report, the authors present a methodology that allows the fuzzy modeling as state equations of a nonlinear multivariable control system. The mathematical formalization of the fuzzy control system is totally developed, without any restrictions, neither in state vector of the plant nor in the control. About the identification of the plant model parameters two techniques have been studied: a classic approach based on gradient descent and a new hybrid modeling technique that uses Ant Colony Optimization (ACO) with gradient descent. |
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