Abstract
Over the last forty years, much attention has been given to the estimation of time variable parameters in models of dynamic systems. This paper outlines how improved estimates of time variable parameters can be obtained using recursive fixed interval smoothing techniques. It then shows how similar techniques can be exploited to estimate state dependent parameter variations and so identify a widely applicable class of nonlinear stochastic systems, which includes chaotic processes.
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