Abstract
Two identification algorithms, a least squares and a correlation analysis based, are developed for dual-rate stochastic systems in which the output sampling period is an integer multiple of the input updating period. The basic idea is to use auxiliary FIR models to predict unmeasurable noise-free (true) outputs, and then use these and system inputs to identify parameters of underlying fast single-rate models. The simulation results indicate that the proposed algorithms are effective. Copyright © 2004 John Wiley & Sons, Ltd.
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