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Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems,and online prediction is always necessary in many real applications.To simultaneously obtain better or acceptable online prediction accuracy and shorter computing time,we propose a new adaptive online method based on least squares support vector regression(LS-SVR).This method adopts two approaches.One approach is that we delete certain support vectors by judging the linear correlation among the samples to increase the sparseness of the prediction model.This approach can control the loss of useful information in sample data,improve the generalization capability of the prediction model,and reduce the prediction time.The other approach is that we reduce the number of traditional LS-SVR parameters and establish a modified simple prediction model.This approach can reduce the calculation time in the process of adaptive online training.Simulation and a certain electric system application indicate preliminarily that the proposed method is an effective prediction approach for its good prediction accuracy and low computing time.
Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems, and online prediction is always necessary in many real applications. To obtain better or acceptable online prediction accuracy and shorter computing time, we propose a new adaptive online method based on least squares support vector regression (LS-SVR). This method employs two approaches. One way is that we delete certain support vectors by judging the linear correlation among the samples to increase the sparseness of the prediction model. This approach can control the loss of useful information in sample data, improve the generalization capability of the prediction model, and reduce the prediction time. The other approach is that we we reduce the number of traditional LS-SVR parameters and establish a modified simple prediction model. This approach can reduce the calculation time in the process of adaptive online training. Simulation and certain electric system applicatio n indicate preliminarily that the proposed method is an effective prediction approach for its good prediction accuracy and low computing time.