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首页> 外文期刊>Signal Processing: The Official Publication of the European Association for Signal Processing (EURASIP) >MODEL STRUCTURE INCORPORATED INTO RECURSIVE PARTIAL REALIZATION STRATEGIES
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MODEL STRUCTURE INCORPORATED INTO RECURSIVE PARTIAL REALIZATION STRATEGIES

机译：MODEL STRUCTURE INCORPORATED INTO RECURSIVE PARTIAL REALIZATION STRATEGIES

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摘要

We present strategies for choosing from various canonical identifier structures in recursive partial realization algorithms for identification of state-space models. The different model structures considered in this paper have different estimation convergence rates. The relative convergence rates of the proposed deterministic algorithms are conjectured to be consistent with our (proposed) measure of the chosen model structure parameter information content. This measure, which we call the relative structural-algorithm time constant, is determined from the Fisher information matrix. This description of relative convergence rates apparently is independent of model order, so that either over- or underdetermined model parameter convergence rates can be characterized by our measure of parameter information. In the proposed stochastic identifiers, the model parameter sensitivity itself is shown to have a significant effect on the initial convergence of the model parameter estimates, while final convergence rates are again strongly connected to the measure of parameter information. Some results on the minimization of the identification criterion are examined, especially those dealing with model structure influence on location and occurrence of the criterion minima. We present a strategy for increasing model convergence rates and estimation accuracy using our relative structural-algorithm time constant. Finally, we compare our method with a recently described iterative method.

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