Robust identification of non-linear greenhouse model using evolutionary algorithms
[EN] This paper presents the non-linear modelling, based oil first principle equations, for a climatic model of a greenhouse and the estimation of the feasible parameter set (FPS) when the identification error is bounded simultaneously by several norms. The robust identification problem is transform...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2008 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/126279 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/126279 |
| Access Level: | acceso abierto |
| Palavra-chave: | Robust identification Multimodal optimization Multiobjective optimization Evolutionary algorithms Greenhouse modelling INGENIERIA DE SISTEMAS Y AUTOMATICA |
| Resumo: | [EN] This paper presents the non-linear modelling, based oil first principle equations, for a climatic model of a greenhouse and the estimation of the feasible parameter set (FPS) when the identification error is bounded simultaneously by several norms. The robust identification problem is transformed into a multimodal optimization problem with an infinite number of global minima that constitute the FPS. For the optimization task, a special evolutionary algorithm (epsilon-GA) is presented, which characterizes the FPS by means of a discrete set of models that are well distributed along the FPS. A procedure for determining the norm bounds, such that FPS not equal 0, is (c) 2007 Elsevier Ltd. All rights reserved. |
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