Exercises – Doctoral school on nonlinear system identification Guest lectures: Nonlinear system identification using sequential Monte Carlo methods
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چکیده
In other words, find the probability density functions f(·) and g(·) in (2) corresponding to the model (1). (b) Simulate the model (1) to produce T = 100 measurements y1:T . Based on these measurements compute the optimal (in the sense that it minimizes the mean square error) estimate of xt | y1:t for t = 1, . . . , T . Implement a bootstrap particle filter and compare to the optimal estimates. You can for example perform this comparison by plotting the root mean square estimate (RMSE) ε(N) as a function of the number of particles used in the particle filter (also plot the RMSE for the optimal estimator in the same figure). The RMSE is defined according to ε(N) , √√√√ 1 T T ∑
منابع مشابه
Exercises – Summer school on foundations and advances in stochastic filtering (FASF 2015) Course 1: Nonlinear system identification using sequential Monte Carlo methods
(b) Simulate the model (1) to produce T = 100 measurements y1:T . Based on these measurements compute the optimal (in the sense that it minimizes the mean square error) estimate of xt | y1:t for t = 1, . . . , T . Implement a bootstrap particle filter and compare to the optimal estimates. You can for example perform this comparison by plotting the root mean square estimate (RMSE) ε(N) as a func...
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