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| Titel: |
A GENETIC NEURAL FUZZY SYSTEM AND ITS APPLICATION IN QUALITY PREDICTION IN THE INJECTION PROCESS |
| Auteur: |
Li, Erguo Jia, Li Yu, Jinshou |
| Verschenen in: |
Chemical engineering communications |
| Paginering: | Jaargang 191 (2004) nr. 3 pagina's 335-355 |
| Jaar: |
2004-03 |
| Inhoud: |
A genetic neural fuzzy system (GNFS) is presented and introduced to quality prediction in the injection process. A hybrid-learning algorithm is proposed, which is divided into two stages to train GNFS. During the first learning stage, the genetic algorithm is used to optimize the structure of GNFS and the membership function of each fuzzy term because of its capability of parallel and global search. On the basis of the first optimized training stages, the back-propagation algorithm (BP algorithm) is adopted to update the parameters of the GNFS to improve its predicting precision and reduce the computation time. The process of constructing a quality prediction model for an injection process based on GNFS is described in detail. The predicted weight of the molded part from the model based on GNFS demonstrates that the proposed GNFS has superior performance and good generalization capability in quality prediction in the injection process. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 1 van 6 gevonden artikelen
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