Support vector machines framework for linear signal processing
Signal Processing
A Fast Algorithm for 2-D ARMA Parameters Estimation
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Online SVR Training by Solving the Primal Optimization Problem
Journal of Signal Processing Systems
Expert Systems with Applications: An International Journal
A unified SVM framework for signal estimation
Digital Signal Processing
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This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on the support vector method (SVM) for identification applications. A statistical analysis of the characteristics of the proposed method is carried out. An analytical relationship between residuals and SVM-ARMA coefficients allows the linking of the fundamentals of SVM with several classical system identification methods. Additionally, the effect of outliers can be cancelled. Application examples show the performance of SVM-ARMA algorithm when it is compared with other system identification methods.