The nature of statistical learning theory
The nature of statistical learning theory
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Using support vector machines for time series prediction
Advances in kernel methods
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
The Journal of Machine Learning Research
An intelligent system for dynamic system state forecasting
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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This study applies a novel neural-network technique, support vector regression (SVR), to predict reliably in dynamical system. The aim of this study is to examine the feasibility of SVR in state prediction by comparing it with the existing neural-network approaches. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using genetic algorithms, and then adopts the optimal parameters to construct the SVR models. The application results of practical vibration data state forecasting measured from a Co2 compressor demonstrate that the GA-SVR model outperforms the existing neural network based on the criteria of mean absolute error (MAE) and roota mean square error (RMSE).