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...
| Autor principal: | |
|---|---|
| Formato: | masterThesis |
| Idioma: | por |
| Publicado em: |
2016
|
| Assuntos: | |
| Texto completo: | https://doi.org/FAGUNDES, Luis Paulo. Algoritmos genéticos aplicados na escolha da taxa de amostragem em identificação de sistemas. 2016. 89 f. Dissertação (Mestrado em Engenharias) - Universidade Federal de Uberlândia, Uberlândia, 2016. Disponível em: http://doi.org/10.14393/ufu.di.2016.271 https://doi.org/http://doi.org/10.14393/ufu.di.2016.271 |
| País: | Brasil |
| Oai: | oai:repositorio.ufu.br:123456789/14628 |
| Resumo: | 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. |
|---|