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Degradation data missing imputation method based on support vector machine and rbf neural network

A support vector machine and degraded data technology, which is applied in biological neural network models, electrical digital data processing, special data processing applications, etc., can solve the problems of performance degradation data missing interpolation, etc., and achieve the effect of convenience

Active Publication Date: 2017-02-08
BEIHANG UNIV
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AI Technical Summary

Problems solved by technology

[0010] The purpose of the present invention is to solve the missing interpolation problem of performance degradation data, and propose a kind of degraded data missing interpolation method based on support vector machine and RBF neural network with strong versatility

Method used

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  • Degradation data missing imputation method based on support vector machine and rbf neural network
  • Degradation data missing imputation method based on support vector machine and rbf neural network
  • Degradation data missing imputation method based on support vector machine and rbf neural network

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Embodiment 1

[0066] Taking a set of simulation data with missing performance degradation as an example, there are 300 complete data, and 120 data are missing in the middle of the data, and the unit has been omitted, such as figure 2 shown. Adopt the degenerate data missing interpolation method based on support vector machine and RBF neural network that the present invention proposes to interpolate its missing data, application steps and method are as follows:

[0067] Step 1, using support vector machine to establish a degradation data trend model;

[0068] Using the LS-SVM toolbox embedded in MATLAB software to establish a degradation trend model, the kernel function uses the RBF kernel function, and the regular parameter gam=2.1090×10 6 , the kernel parameter sig2=30.9913, with Y obs with T obs As the training data, the degradation trend model f(t) is obtained. Then through the obtained degradation trend model f(t), the T mis As input, compute the trend sequence Q for missing data ...

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Abstract

The invention discloses a degradation data missing interpolation method based on a support vector machine and an RBF neural network. The method includes the following steps that firstly, a degeneration data trend model is established by means of the support vector machine; secondly, residual error sequences of observed degradation data are calculated; thirdly, the RBF neural network is set up, and the network is trained by means of the residual error sequences of the observed degradation data; fourthly, residual error sequences of missing data are estimated through the trained RBF neural network; fifthly, trend terms of the missing data and estimation results of the residual error sequences are merged, so that a degradation data interpolation result is obtained. A support vector machine method and an RBF neural network method are combined to obtain the degradation data missing interpolation method, and the problem of interpolation of performance missing degradation data in an accelerated degradation test is solved.

Description

technical field [0001] The invention relates to a degraded data missing interpolation method based on a support vector machine and an RBF neural network, and belongs to the technical field of accelerated degraded tests. Background technique [0002] In the data collection of accelerated degradation tests, due to the failure of monitoring equipment or the fault of manual recording personnel, the collected performance degradation data is often missing. The lack of data has caused difficulties in subsequent performance degradation data processing. In the data processing and evaluation of accelerated degradation tests, fault prediction or life prediction, complete data is required as input. In addition, many traditional performance degradation data processing methods cannot handle There are missing data for statistical analysis. For example, some algorithms about time series require the input data to be a complete equidistant data set. In life prediction or failure prediction, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00G06N3/02
Inventor 孙富强范晔李晓阳姜同敏
Owner BEIHANG UNIV
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