Algoritmos genéticos aplicados na escolha da taxa de amostragem em identificação de sistemas
The present work has as the main goal to introduce a new method to select the sample time of input and output signals used in the identification process using NARMAX representation. To achieve this goal is proposed a genetic algorithm wich uses a supersampled signal, i.e., a signal sampled in the mo...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Brasil |
| Institución: | Universidade Federal de Uberlândia (UFU) |
| Repositorio: | Repositório Institucional da UFU |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufu.br:123456789/14628 |
| Acceso en línea: | https://repositorio.ufu.br/handle/123456789/14628 http://doi.org/10.14393/ufu.di.2016.271 |
| Access Level: | acceso abierto |
| Palabra clave: | Identificação de sistemas não lineares Algoritmos genéticos Célula combustível NARMAX Sistemas não lineares Nonlinear system identification Genetic algorithm Fuel cell CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
| Sumario: | The present work has as the main goal to introduce a new method to select the sample time of input and output signals used in the identification process using NARMAX representation. To achieve this goal is proposed a genetic algorithm wich uses a supersampled signal, i.e., a signal sampled in the most high frequency available, and later decimation rates are used to create different individuals from the high frequency sample signal. The individuals evaluation uses a system identification with NARMAX representation. The evaluation of the proposed method used a genetic algorithm developed in the software Matlab®. The proposed method was applied in the process identification of a polimeric membrane fuel cell temperature model and the results are presented. |
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