Reliability forecasting by recurrent Support Vector Regression

Abstract

Support Vector Regression (SVR), which employs the structural risk minimisation principle to minimise an upper bound of the generalisation errors instead of minimising the training errors used by ANNs, has been successfully applied to solve nonlinear forecasting and times series problems. However, the application of SVR to reliability forecasting has still not been extensively explored. In general, Recurrent Neural Networks (RNNs) are trained by back-propagation algorithms. In the study, the learning algorithms of SVR are applied to RNNs for forecasting system reliability, and the Immune Algorithm (IA) is applied to the parameter determining the SVR model. A numerical example in the existing literature is employed to demonstrate the prediction performance of the proposed model. Empirical results illustrate that the proposed model outperforms other approaches in the existing literature.