A support vector machine fault prediction method based on adaptive ant lion optimization

Abstract

The invention discloses a support vector machine fault prediction method based on adaptive ant lion optimization. The method comprises the following steps of step 1, preprocessing is carried out; step2, ant lion parameters are initialized; step 3, a support vector machine regression model is trained; step 4, fitness is calculated according to a test set; and step 5, a life curve is predicted by using a single-step loop iteration method. According to the method, the historical data in a period of time when degradation of a monitoring circuit occurs serve as training samples, the number of thesamples is limited, at the moment, when a traditional multi-step prediction method is adopted for predicting characteristic parameter values, a plurality of prediction values at the future moment areestimated at the same time, and prediction errors are gradually increased along with increase of the prediction distance. According to the method, a new predicted value is used for replacing an initial value at an earlier moment in each cycle to serve as a training sample, the sample is updated in real time, the accumulative errors are reduced, and the error value of each prediction is reduced tothe minimum, so that the alculation results are more accurate.

Description

Technical field: [0001] The invention relates to a single-step loop iterative fault prediction method (IAALO-SVM) based on an adaptive antlion optimized support vector machine. By optimizing the parameters in the support vector machine, the degradation of electronic products under different conditions can be obtained Lifetime belongs to the technical field of system engineering system reliability. Background technique: [0002] Support vector machine is a machine learning method based on statistical learning developed in recent years. It is based on statistical learning theory, starting from a small sample, using the structural risk minimization criterion, while minimizing the error of the sample point, considering the structural factors of the model, and fundamentally improving the generalization ability. At present, it is mainly used in pattern classification and nonlinear regression problems. Due to its superior learning ability, it has attracted more and more attention ...

AI Technical Summary

Problems solved by technology

However, the type of model kernel function and the selection of parameters in the kernel function have a great influence on the prediction results.

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