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| Received:August 25, 2010Revised:March 01, 2011 |
| 基金项目:The research of B. G. Fitzpatrck was partly supported by the Joint Technology Office and the Air Force Office of Scientific Research through the Multidisciplinary Research Initiative (No. F49620-02-1-0319), and the Air Force Office of Scientific Research (No. FA9550-09-1-0524). The research of G. Yin was partly supported by the Air Force Office of Scientific Research (No. FA9550-10-1-0210), and partly by the Natural Science Foundation of China (No. 70871055). The research of L. Wang was partly by supported by the Air Force Office of Scientific Research (No. FA9550-10-1-0210). |
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| Convergence and error bounds of adaptive filtering under model structure and regressor uncertainties |
| Ben G. FITZPATRICK,Gang G. YIN,Le Yi WANG |
| (Tempest Technologies 8939 South Sepulveda Boulevard; Department of Mathematics, Loyola Marymount University;Department of Mathematics, Wayne State University;Department of Electrical and Computer Engineering, Wayne State University) |
| Abstract: |
| Adaptive filtering algorithms are investigated when system models are subject to model structure errors and regressor signal perturbations. System models for practical applications are often approximations of high-order or nonlinear systems, introducing model structure uncertainties. Measurement and actuation errors cause signal perturbations, which in turn lead to uncertainties in regressors of adaptive filtering algorithms. Employing ordinary differential equation (ODE) methodologies, we show that convergence properties and estimation bias can be characterized by certain differential inclusions. Conditions to ensure algorithm convergence and bounds on estimation bias are derived. These findings yield better understanding of the robustness of adaptive algorithms against structural and signal uncertainties. |
| Key words: Adaptive filtering Structural uncertainties Robustness Estimation bias |
