Uncertainty Evaluation Method of Complex Nonlinear System Test Based on Support Vector Machine Model

Measurement uncertainty analysis of complex nonlinear system test is of great significance to ensure the quality and reliability of products. As a typical complex nonlinear system test, the whiplash test which evaluates the vehicle seat head restraint on the occupant neck protection effect is considered, and an uncertainty evaluation method based on support vector machine model is suggested. The probability density functions of main influence factors of whiplash test is studied,and design of experiments is implemented by using latin hypercube sampling method. The experiment results are utilized to construct the mathematical model of whiplash test based on the least squares support vector machine. The design result is compared with that of Back Propagation artificial neural networks mathematical model, and is shown that the prediction accuracy resulted from the least squares support vector machine is higher, which meets the follow-up assessment requirement. The evaluation of uncertainty of whiplash test is realized by using the Monte Carlo method. By the comparison of uncertainty evaluation based on the method specified by national standard《Guide to the Expression of Uncertainty in Measurement》, the suggested method is more accurate and reliable for complex nonlinear system test. The suggested method can be widely applied to the uncertainty analysis for various complex tests of products.