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Plant identification via adaptive combination of transversal filters. (English) Zbl 1172.94353

Summary: For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during stationary periods. Some plant identification simulation examples show the effectiveness of our method when compared to previous variable step size approaches. This combination approach can be straightforwardly extended to other kinds of filters, as it is illustrated with a convex combination of recursive least-squares (RLS) filters.

MSC:

94A12 Signal theory (characterization, reconstruction, filtering, etc.)
93E10 Estimation and detection in stochastic control theory
93E11 Filtering in stochastic control theory
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