Publicação
Artificial neural networks and neuro-fuzzy systems for modelling and controlling real systems: a comparative study
| Resumo: | This article presents a comparison of artificial neural networks andneuro-fuzzy systems appliedfor modelling andcontrolling a real system. The main objective is to model and control the temperature inside of a kiln for the ceramic industry. The details of all system components are described. The steps taken to arrive at the direct and inverse models using the two architectures: adaptive neuro fuzzy inference system and feedforward neural networks are described and compared. Finally, real-time control results using internal model control strategy are resented. Using available Matlab software for both algorithms, the objective is to show the implementation steps for modelling and controlling a real system. Finally, the performances of the two solutions were comparedthrough different parameters for a specific real didactic case |
|---|---|
| Autores principais: | Vieira, José Barros |
| Outros Autores: | Dias, Fernando; Mota, Alexandre |
| Assunto: | Temperature control Fuzzy hybridsystems Artificial neural networks Applied neuro-fuzzy control Model-based control Real-time control |
| Ano: | 2004 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Castelo Branco |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico de Castelo Branco |
| Resumo: | This article presents a comparison of artificial neural networks andneuro-fuzzy systems appliedfor modelling andcontrolling a real system. The main objective is to model and control the temperature inside of a kiln for the ceramic industry. The details of all system components are described. The steps taken to arrive at the direct and inverse models using the two architectures: adaptive neuro fuzzy inference system and feedforward neural networks are described and compared. Finally, real-time control results using internal model control strategy are resented. Using available Matlab software for both algorithms, the objective is to show the implementation steps for modelling and controlling a real system. Finally, the performances of the two solutions were comparedthrough different parameters for a specific real didactic case |
|---|