SVM solar wing unfolding reliability evaluation method based on kernel optimization

A technology of support vector machine and kernel function, which is applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of low evaluation accuracy, achieve good promotion ability, and solve the effect of dimensionality problem

Inactive Publication Date: 2012-06-13
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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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

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  • SVM solar wing unfolding reliability evaluation method based on kernel optimization
  • SVM solar wing unfolding reliability evaluation method based on kernel optimization
  • SVM solar wing unfolding reliability evaluation method based on kernel optimization

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Embodiment Construction

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0030] The overall process of the present invention is as figure 1 shown.

[0031] Step 1: Establish a comprehensive evaluation index system for solar wing deployment reliability based on expert knowledge

[0032] The comprehensive evaluation index system of solar wing deployment reliability includes four first-level indexes: fundamental frequency of deployment state, minimum static moment margin, comprehensive index of hinge driving characteristics and comprehensive index of deployment test. Among them, the comprehensive index of hinge driving characteristics includes the following four second-level indexes: root hinge moment, support arm / connector hinge moment, connector frame / inner plate hinge moment, and inner plate / outer plate hinge moment. The comprehensive index of the deployment test includes the following five second-level indicators: deployment ti...

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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 ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 皮德常王娟
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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