Book ChapterDOI
Application Research of Support Vector Machines in Dynamical System State Forecasting
Guangrui Wen,Jianan Yin,Xining Zhang,Ying Jin +3 more
- pp 712-719
TLDR
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.Abstract:
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.read more
Citations
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Aplikace lokálních aproximátorů pro řízení reálného mechatronického systému
TL;DR: Results shows that local-models based control have improved regulation in comparison with PID used alone and that it is adaptable, and its utilization by MCUs providing sample frequency up to 1 kHz seems to be very advantageous.
Journal Article
An intelligent system for dynamic system state forecasting
TL;DR: From the forecasting tests and simulation analyses, it is found that the developed NF system is a very reliable prognostic scheme; it can capture system dynamic behavior quickly, and track system responses accurately.
References
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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Journal ArticleDOI
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Journal ArticleDOI
Application of support vector machines in financial time series forecasting
Francis E. H. Tay,Lijuan Cao +1 more
TL;DR: Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast financial time series because of the variability in performance with respect to the free parameters.