SVM solar wing unfolding reliability evaluation method based on kernel optimization

Abstract

The invention discloses a SVM (Support Vector Machine) solar wing unfolding reliability evaluation method based on kernel optimization. The method comprises the following steps of: establishing a solar wing unfolding reliability comprehensive evaluation index system according to expert knowledge; obtaining a weight vector of the evaluation index system by a matter element method and an analytic hierarchy process; grading measured values of each factor influencing the unfolding of the solar wing by experts, and taking the grading result as sample data; automatically selecting the SVM kernel and parameter values thereof by a program so as to form a training model; performing cross validation to check whether the kernel and the parameters thereof need to be regulated finely; and validating the formed model by a test sample, and evaluating the reliability of unfolding of the solar wing. The method has the following advantages that: the evaluation result is objective and credible under circumstances of zero failure, small sample, nonlinearity, high dimensionality and the like.

Description

technical field [0001] The invention relates to a support vector machine solar wing deployment reliability evaluation method based on kernel function optimization, in particular to a solar wing deployment reliability evaluation method for small sample reliability test data. Background technique [0002] With the rapid development of aerospace technology, the structure of spacecraft is becoming more and more complex, and its functions are increasing. Various mechanisms are required to complete various tasks. As the main component of the satellite, whether the solar wing can be deployed smoothly is a prerequisite for the normal operation of the satellite, but due to its high cost, it is impossible to conduct a large number of deployment tests. In solving the problem of small samples, the support vector machine method based on statistical learning theory has great advantages. [0003] Statistical learning theory starts from the idea of ​​controlling the complexity of learning ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem of analyzing the reliability of solar wing deployment in the case of small samples, and introduce the method of support vector machine into the analysis of product reliability, so as to solve the problem of low evaluation accuracy in the case of small samples

Images