کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
9653367 679045 2005 24 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Fast error whitening algorithms for system identification and control with noisy data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Fast error whitening algorithms for system identification and control with noisy data
چکیده انگلیسی
Linear system identification with noisy input/output is a critical problem in signal processing and control. Conventional techniques based on the mean squared-error (MSE) criterion can at best provide a biased parameter estimate of the unknown system being modeled. Recently, we proposed a new criterion called the error whitening criterion (EWC) to solve the problem of linear parameter estimation in the presence of additive white noise. Accordingly, the central idea is to partially whiten the error signal beyond a predetermined correlation lag. In the first-half of the paper, we will derive a fast Quasi-Newton type recursive algorithm to compute the optimal EWC solution in an online manner. The algorithm has O(N2) complexity where, N represents the length of the parameter vector to be estimated. One of the primary limitations of EWC is the assumption that the input noise must be white. In the second-half of this paper, we will introduce a modified cost function that overcomes this assumption and allows the noise in the input to be colored. The analysis of this modified cost function is then presented followed by a sample-by-sample stochastic gradient algorithm to optimally compute the analytical solution. Finally, we will show the experimental results with EWC as well as the modified criterion in system identification and controller design problems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 69, Issues 1–3, December 2005, Pages 158-181
نویسندگان
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