System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Development of omni-directional correlation functions for nonlinear model validation
Automatica (Journal of IFAC)
A set of novel correlation tests for nonlinear system variables
International Journal of Systems Science
A correlation-test-based validation procedure for identified neural networks
IEEE Transactions on Neural Networks
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The selection of hyper-parameters is a crucial challenge in Support Vector Machine modeling. Differed from using basic statistics of residuals in previous method, the new approach selects hyper-parameters by checking whether or not there is information redundancy in residual sequence. Furthermore, Omni-Directional Correlation Function (ODCF) is applied to test redundancy in residual, and the proof of the accuracy of the methodology is given in terms of numerical demonstration. Experiments conducted on benchmark time series, annual sunspot number and Mackey-Glass time series; indicate that the proposed method has better performance than the recorded in previous literatures.