A modified stochastic gradient based parameter estimation algorithm for dual-rate sampled-data systems ?
In this paper, we propose a novel identification algorithm for a class of dual-rate sampled-data systems whose inputoutput data are measured by two different sampling rates. A polynomial transformation technique is employed to derive a mathematical model for such dual-rate systems. The proposed modified stochastic gradient algorithm has faster convergence rate than stochastic gradient algorithms for parameter identification using the dual-rate inputoutput data. Convergence properties of the algorithm are analyzed. Finally, illustrative and comparison examples are provided to verify the effectiveness and performance improvement of the proposed method.
stochastic gradient parameter estimation dual-rate systems convergence properties