Abstract
Fault or health condition prediction of the complex systems has attracted more attention in recent years. The complex systems often show complex dynamic behavior and uncertainty, which makes it difficult to establish a precise physical model. Therefore, the time series of complex system is used to implement prediction in practice. Aiming at time series online prediction, we propose a new method to improve the prediction accuracy in this paper, which is based on the grey system theory and incremental learning algorithm. In this method, the accumulated generating operation (AGO) with the raw time series is taken to improve the data quality and regularity firstly; then the prediction is conducted by a modified LS‐SVR model, which simplifies the calculation process with incremental learning; finally, the inverse accumulated generating operation (IAGO) is performed to get the prediction results. The results of the prediction experiments indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.
Highlights
Prediction technique is essential to ensure the operational safety of complex systems
Least Squares Support Vector Regression LS-SVR was proposed by Suykens et al 11 In LS-SVR, the inequality constrains are replaced by equality constrains, which can reduce the calculation time effectively
In order to validate the performance of the proposed AGO-based method described in Section 3.1 and the modified LS-SVR model-based incremental learning described in Section 3.2, we perform two simulation experiments
Summary
Prediction technique is essential to ensure the operational safety of complex systems. The complex systems are not easy to establish their precise physical models. Time series-based prediction methods have attracted increased attention 1–7. Compared with other reported methods, Support Vector Regression SVR is based on statistical theory and structural risk minimization principle 8–10 , and it has a global optimum and exhibits better accuracy in nonlinear and nonstationary time series data prediction via kernel function. Mathematical Problems in Engineering programming QP problem becomes more complex. Least Squares Support Vector Regression LS-SVR was proposed by Suykens et al 11 In LS-SVR, the inequality constrains are replaced by equality constrains, which can reduce the calculation time effectively. LS-SVR has more attention in time series forecasting 12–16
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