The nature of statistical learning theory
The nature of statistical learning theory
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Using support vector machines for time series prediction
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
An intelligent system for dynamic system state forecasting
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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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.