Research on Forecasting Method Based on Genetic Algorithms and Support Vector Machines
| Autor: | Cheng Yong Xiao, Zhi Peng Feng, Yong Sheng Deng, Peng Yan Guo |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Engineering
Structured support vector machine business.industry Generalization General Medicine Machine learning computer.software_genre Relevance vector machine Support vector machine Dimension (vector space) Margin classifier Least squares support vector machine Embedding Artificial intelligence Data mining business computer |
| Zdroj: | Applied Mechanics and Materials. :2603-2607 |
| ISSN: | 1662-7482 |
| Popis: | State forecast of machine using support vector machines has good generalization ability in situation of rare samples. Appropriate parameter selection is very crucial to the learning results and generalization ability of support vector machines. In addition, embedding dimension influences the phase space reconstitution of nonlinear systems, as well as the precision of machine state forecasting. In this paper, an approach to optimize the parameters of SVM and the embedding dimension based on genetic algorithms was proposed. The proposed model is applied to the tendency forecasting of the vibration of shovel electric drive system. The results show that it can avoid blindness of manually selection of parameters and meanwhile improves the prediction performance greatly. |
| Databáze: | OpenAIRE |
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