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A multiinnovation least-squares (MILS) identification algorithm is presented for linear regression models with unknown parameter vectors by expanding the innovation length in the traditional recursive least-squares (RLS) algorithm from the viewpoint of innovation modification. Because the proposed MILS algorithm uses p innovations (not only the current innovation but also past innovations) at each...
This paper develops a hierarchical extended stochastic gradient identification algorithms for MIMO ARMAX-like systems to deal with colored noises based on the hierarchical identification principle. The convergence performance of such algorithms is studied in detail; in particular, conditions for parameter estimation errors to converge to zero are established, which include persistent excitation of...
This paper studies identification problems for two-input multirate systems with colored noises (The method in the paper can be easily extended to multi-input multirate systems). The state-space models are derived for the multirate systems with two different input sampling periods and furthermore the corresponding transfer functions are obtained. To solve the difficulty of identification models with...
A multi-innovation least-squares (MILS) identification algorithm is presented for linear regression models with unknown parameter vectors by extending the conventional standard least-squares (LS) algorithm from the viewpoint of innovation modification. Because the proposed MILS algorithms use p innovations at each iteration (the integer p > 1 being an innovation length), the accuracy of parameter...
This paper applies the multi-innovation identification theory to study the parameter estimation problem for CARMA models, and presents the multi-innovation extended stochastic gradient algorithm by expanding the scalar innovation to an innovation vector, and analyzes and proves the convergence properties of the algorithms involved. The simulation results show that the proposed algorithms are effective.
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