Nonlinear System Identification Using Particle Filters.
作者： Thomas B. Schön
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摘要: Particle filters are computational methods opening up for systematic inference in nonlinear/non-Gaussian state space models. The particle constitute the most popular sequential Monte Carlo (SMC) methods. This is a relatively recent development and aim here to provide brief exposition of these SMC how they key enabling algorithms solving nonlinear system identification problems. important both frequentistic (maximum likelihood) Bayesian identification.
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