Application research of support vector machines in dynamical system state forecasting
发布时间：2025-04-30
点击次数：
- 发布时间：
- 2025-04-30
- 论文名称：
- Application research of support vector machines in dynamical system state forecasting
- 发表刊物：
- ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPE
- 摘要：
- This paper deals with the application of a novel neural network technique, support vector machines (SVMs) and its extension support vector regression (SVR), in state forecasting of dynamical system. The objective of this paper is to examine the feasibility of SVR in state forecasting by comparing it with a traditional BP neural network model. Logistic time series are used as the experiment data sets to validate the performance of SVR model. The experiment results show that SVR model outperforms the BP neural network based on the criteria of normalized mean square error (NMSE). Finally, application results of practical vibration data state forecasting measured from some CO2 compressor company proved that it is advantageous to apply SVR to forecast state time series and it can capture system dynamic behavior quickly, and track system responses accurately.
- 合写作者：
- Wen, Guangrui; Yin, Jianan; Zhang, Xining; 等
- 是否译文：
- 否
- 发表时间：
- 2008-09-15