Sensor Dynamic Modeling Based on LS-SVM and NGA
Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.
Wei Gao, Yasuhiro Takaya, Yongsheng Gao and Michael Krystek
Q. Wang et al., "Sensor Dynamic Modeling Based on LS-SVM and NGA", Key Engineering Materials, Vols. 381-382, pp. 439-442, 2008