Results 151 to 160 of about 192,666 (197)

Forgetting-Factor Regrets for Online Convex Optimization

IEEE Transactions on Automatic Control
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yuhang Liu, Wenxiao Zhao, George Yin
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Convergence of forgetting factor least square algorithms

2001 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (IEEE Cat. No.01CH37233), 2002
Convergence of the forgetting factor least square (FFLS) algorithm is analyzed by using stochastic process theory; and the upper bound of the parameter estimation error is derived. For time-varying stochastic systems, the FFLS algorithm is capable of tracking the time-varying parameters and the parameter estimation error is bounded.
null Feng Ding, null Tao Ding
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Stochastic LMS with self adaptive forgetting factor

Conference Proceeding IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 2003
A least-mean-square algorithm, the stochastic gradient search least-mean-square (SGSLMS) algorithm, is proposed. It is robust to noise in the gradient estimate and has fast convergence without having to use an optimal step size. The SGSLMS algorithm is realized by estimating the correlation between the adaptive error and the input signals using as ...
S. Chung, P. McLeod
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Robust harmonic estimation using Forgetting Factor RLS

2011 Annual IEEE India Conference, 2011
The prime reasons for power quality degradation include voltage sag, swell and momentary interruptions and also the presence of harmonics. Thus accurate computation of harmonics is really a challenging problem in power system. Many algorithms have been proposed for harmonic estimation to improve the power quality.
H. K. Sahoo, Pooja Sharma, N. P. Rath
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Exponential forgetting factor observer in discrete time

Systems & Control Letters, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ticlea, Alexandru, Besancon, Gildas
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Statistical properties of support vector machines with forgetting factor

Neural Networks, 2012
Introducing a forgetting factor allows a support vector machine to solve time-varying problems adaptively. However, the exponential forgetting factor proposed in an earlier work does not ensure convergence of average generalization error even for a simple linearly separable problem.
Funaya, Hiroyuki, Ikeda, Kazushi
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A directional forgetting factor for single-parameter variations

Proceedings of 1995 American Control Conference - ACC'95, 2005
Use of the recursive least-squares algorithm to track parameters that may undergo slow or sudden changes requires refinements to prevent excessive gain decay and the loss of the ability to re-identify parameters. A technique in wide-spread use developed by Fortescue, Kershenbaum and Ydstie (1981) involves variable weighting of past data based on the ...
W.W. Woo, S.A. Svoronos, O.D. Crisalle
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The assessment factor most nurses forget.

RN, 1989
Wakefulness, temperature, and cardiovascular function are just three of more than a hundred patterns that the human body follows every day. You can use the latest findings on these biological rhythms to deliver better care.
C, Fraser, M J, Filler
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A Targeted Forgetting Factor for Recursive Least Squares

2018 IEEE Conference on Decision and Control (CDC), 2018
Recursive least squares (RLS) is widely used in signal processing, identification, and control, but is plagued by the inability to adjust quickly to changes in the unknown parameters. RLS with standard forgetting factor overcomes this problem but causes divergence due to the lack of persistency.
Ankit Goel, Dennis S Bernstein
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Formal proof for the constant trace forgetting factor

International Journal of Control, 1989
In recent literature, the bounds on a constant trace forgetting factor used with a standard weighted least-squares identification have been stated, based on heuristic considerations. A formal proof for these bounds is presented.
A. VIEN, R. K. WOOD
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