Prediction for ATE State Parameters Based on Improved LS-SVM

摘要：

　　An imaproved least squares support vector machines (LS-SVM) was proposed to improve the sparse and robust performance of LS-SVM in the small samples prediction.The sparse and robust performance could be improved through adding elements of weighted LS-SVM and robust LS-SVM.We introduced a contrast experiment for ATE parameters prediction control through the three methods of neural network, LS-SVM and improved LS-SVM algorithm.Simulation results show that the improved LS-SVM algorithm has good performance in ATE parameters prediction, which succeeds in stability assessment for an aviation ATE.

关键词： Improved LS-SVM ATE Parameter prediction Non-linear

作者: Mao Hongyu An Shaolong Zhu Yuchuan Hu Zhuolin

作者单位: Aeronautical Equipment Measurement Master Station,Beijing 100070,China

会议类型: 国际会议

会议名称: 2013 IEEE 11th International Conference on Electronic Measurement & Instruments(第十一届IEEE国际电子测量与仪器学术会议)

会议地点: 哈尔滨

会议语种:英文

页码: 710-713

在线出版日期: 2013-08-16（万方平台首次上网日期，不代表论文的发表时间）