کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
446875 1443219 2009 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Convergence analysis of a continuous-time nonlinear LMS-type algorithm using stochastic calculus
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر شبکه های کامپیوتری و ارتباطات
پیش نمایش صفحه اول مقاله
Convergence analysis of a continuous-time nonlinear LMS-type algorithm using stochastic calculus
چکیده انگلیسی

In several applications least mean square (LMS) has served as a good tool for estimating the parameters of linear models but the success of continuous-time in nonlinear models has not reached its height. In this paper, we have developed a nonlinear continuous-time LMS type algorithm that estimates parameters of nonlinear systems considering the noisy input–output relationship. The nonlinear system has been assumed to be memoryless and an additive Gaussian noise component to the system has been assumed. The mean squared error between the true system output and the estimated output, when the estimated output is modeled using the same form of the nonlinear function as the original system but with the parameters unknown, is minimized using the gradient scheme with the expectation removed. The result is a least mean square algorithm for nonlinear systems. In particular, we have performed a convergence analysis of the continuous-time nonlinear LMS algorithm applied to nonlinear systems when the time step goes to zero. The resulting algorithm then behaves as a stochastic differential equation, and the standard methods of Itô calculus and Fokker–Planck theory are applied to obtain statistical properties of the mean and covariance evolution of the parameter estimates. Computer simulations corroborate the theoretical results.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: AEU - International Journal of Electronics and Communications - Volume 63, Issue 7, July 2009, Pages 576–583
نویسندگان
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