Results 251 to 260 of about 1,332,719 (289)

Convergence of an adaptive filter with signed filtered error

IEEE Transactions on Signal Processing, 1994
The need for high-speed adaptive filters has prompted the search for alternatives to the popular LMS algorithm. One modification is the replacement of the prediction error term in the LMS update kernel by its signum function. At the same time, in noise and echo cancellation problems, reduced residual noise variance often requires error filtering.
Soura Dasgupta, Jeffery S. Garnett, C. Richard Johnson Jr.   +2 more
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On the convergence of Bayesian adaptive filtering

Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005., 2006
Standard adaptive filtering algorithms, including the popular LMS and RLS algorithms, possess only one parameter (stepsize, forgetting factor) to adjust the tracking speed in a nonstationary environment. Furthermore, existing techniques for the automatic adjustment of this parameter are not totally satisfactory and are rarely used.
Tayeb Sadiki, Dirk T. M. Slock
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Convergent algorithms for collaborative filtering

Proceedings of the 4th ACM conference on Electronic commerce, 2003
A collaborative filtering system analyzes data on the past behavior of its users so as to make recommendations --- a canonical example is the recommending of books based on prior purchases. The full potential of collaborative filtering implicitly rests on the premise that, as an increasing amount of data is collected, it should be possible to make ...
Jon M. Kleinberg, Mark Sandler 0002
openaire   +1 more source

Convergence behavior and convergence rates of stack filters

1991 IEEE International Symposium on Circuits and Systems (ISCAS), 1991
The convergence behavior of type-0 through type-3 stack filters is investigated. It is shown that stack filters of type-0 through type-2 all possess the convergence property; that is, they filter any input signal to a root after consecutive passes of the filter under any appending strategy.
M. Gabbouj, P.-T. Yu, E.J. Coyle
openaire   +1 more source

A globally convergent adaptive IIR filter

2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2002
An open issue in Adaptive IIR Filtering (AIF) is that of convergence to a global minimum in the presence of observation noise, when the system is insufficiently modeled, or when the excitation source is colored. It is well known that algorithms based on Equation Error (EE) contain a single minimum that may be biased whereas, algorithms based on Output ...
Afshin David, Tyseer Aboulnasr
openaire   +1 more source

Asymptotic convergence of the ensemble Kalman filter

2008 15th IEEE International Conference on Image Processing, 2008
This paper formally addresses the asymptotic convergence of the ensemble Kalman filter (EnKF), a state estimation procedure that, when combined with a technique called localization, provides computationally tractable solutions to large-dimensional state estimation problems.
Mark D. Butala, Jonghyun Yun, Yuguo Chen, Richard A. Frazin, Farzad Kamalabadi   +4 more
openaire   +1 more source

Convergence of Kalman filter with quantized innovations

2010 11th International Conference on Control Automation Robotics & Vision, 2010
This work provides a convergence analysis for the estimate error covariance of Kaiman filtering based on quantized measurement innovations (QIKF). By taking the quantization errors as random perturbations in observation system, an equivalent state-observation system is given.
Jian Xu, Jianxun Li, Jiayun Wu
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Convergence properties of a decentralized Kalman filter

2008 47th IEEE Conference on Decision and Control, 2008
We consider the problem of decentralized Kalman filtering in a sensor network. Each sensor node implements a local Kalman filter based on its own measurements and the information exchanged with its neighbors. It combines the information received from other sensors through using a consensus filter as proposed in [14].
Kamgarpour, Maryam, Tomlin, Claire
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Semi-convergence of filters and nets

Mathematical Journal of Okayama University, 1999
Summary: In [Am. Math. Mon. 70, 36-41 (1963; Zbl 0113.16304)] \textit{N. Levine} introduced the concept of semi-open set and semi-continuity. Semi-convergence and semi-compactness were first introduced, investigated and characterized by \textit{C. Dorsett} in [Ann. Soc. Sci. Brux., Ser. I 92, 143-150 (1978; Zbl 0408.54009)] and [Indian J. Mech.
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On the convergence of the LMS algorithm in adaptive filtering

Signal Processing, 2004
An independence assumption on the input vectors is commonly used when stating the convergence of the least mean square algorithm is adaptive filtering. From this hypothesis a range in which the convergence factor must be chosen is determined. In this paper the independence assumption, unrealistic in the case of adaptive filtering, is avoided.
Maria Inés Troparevsky, Carlos E. D'Attellis   +1 more
openaire   +1 more source