کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
560058 1451724 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Performance analysis of multi-innovation stochastic Newton recursive algorithms
ترجمه فارسی عنوان
تجزیه و تحلیل عملکرد الگوریتم های بازگشت نوآورانه نیوتون چند نوآوری
کلمات کلیدی
شناسایی سیستم، الگوریتم بازگشتی، چند نوآوری، تجزیه و تحلیل همگرایی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی

The stochastic Newton recursive algorithm is studied for system identification. The main advantage of this algorithm is that it has extensive form and may embrace more performance with flexible parameters. The primary problem is that the sample covariance matrix may be singular with numbers of model parameters and (or) no general input signal; such a situation hinders the identification process. Thus, the main contribution is adopting multi-innovation to correct the parameter estimation. This simple approach has been proven to solve the problem effectively and improve the identification accuracy. Combined with multi-innovation theory, two improved stochastic Newton recursive algorithms are then proposed for time-invariant and time-varying systems. The expressions of the parameter estimation error bounds have been derived via convergence analysis. The consistence and bounded convergence conclusions of the corresponding algorithms are drawn in detail, and the effect from innovation length and forgetting factor on the convergence property has been explained. The final illustrative examples demonstrate the effectiveness and the convergence properties of the recursive algorithms.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Digital Signal Processing - Volume 56, September 2016, Pages 15–23
نویسندگان
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