Results 231 to 240 of about 2,983,370 (285)

A variable step size LMS algorithm

IEEE Transactions on Signal Processing, 1992
A least-mean-square (LMS) adaptive filter with a variable step size is introduced. The step size increases or decreases as the mean-square error increases or decreases, allowing the adaptive filter to track changes in the system as well as produce a small steady state error. The convergence and steady-state behavior of the algorithm are analyzed.
Kwong, Raymond H., Johnston, Edward W.
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Nonparametric Variable Step-Size LMAT Algorithm

Circuits, Systems, and Signal Processing, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guan, Sihai, Li, Zhi
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VARIABLE STEP-SIZE STÖRMER METHODS

Computational Methods in Sciences and Engineering 2003, 2003
Although it is possible to integrate a special second-order differential equation of the form y''(x) = f(x,y(x)), y(x0) = y0, y'(x0) = y'0 (1) by reducing it to a first order system and applying one of the methods available for those systems, it seems more natural to provide numerical methods to integrate (1) directly without using first derivatives ...
H. RAMOS, J. VIGO-AGUIAR
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The stability of variable step-size LMS algorithms

IEEE Transactions on Signal Processing, 1999
Variable step-site LMS (VSLMS) algorithms are a popular approach to adaptive filtering, which can provide improved performance while maintaining the simplicity and robustness of conventional fixed step-size LMS. Here, we examine the stability of VSLMS with uncorrelated stationary Gaussian data.
Gelfand, Saul B., Wei, Yongbin, Krogmeier, James V.   +2 more
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A variable step size LMS algorithm

Proceedings of the 33rd Midwest Symposium on Circuits and Systems, 2002
A variable step size LMS algorithm is proposed. The variable step size LMS algorithm has a big step size at the beginning, for a maximum convergence speed, and a much smaller step size after the convergence, for a minimum residual error. The algorithm is derived according to the shortest distance norm between the Kalman gain and the LMS gain vectors ...
W.Y. Chen, R.A. Haddad
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Variable Step Size Norm-Constrained Adaptive Filtering Algorithms

Circuits, Systems, and Signal Processing, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shi, Long, Zhao, Haiquan
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A modified variable degree variable step size LMS algorithm

1998 Midwest Symposium on Circuits and Systems (Cat. No. 98CB36268), 2002
In this paper, the data reusing technique which utilizes the LMS algorithm is further investigated. It is shown that a near-optimal performance can be achieved if the number of data reusing runs, M, is made variable. Two modifications are proposed in this paper.
M.I. Haddad, M.A. Khasawneh
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The convex variable step-size (CVSS) algorithm

IEEE Signal Processing Letters, 2000
This letter introduces the convex variable step-size (CVSS) algorithm. The convexity of the resulting cost function is guaranteed. Simulations presented show that with the proposed algorithm, we obtain similar results, as with the VSS algorithm in initial convergence, while there are potential performance gains when abrupt changes occur.
Rusu, C., Cowan, Colin
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A Variable Step Size Decoupled State Estimator

IEEE Transactions on Power Apparatus and Systems, 1979
This paper describes an improved decoupled static-state estimator based on minimization of weighted least squares of the residuals (WLS). The basic idea of P-?, Q-V decoupling of the fast decoupled load flow for node injections is extended to line flows. The solution is obtained alternately iterating the real and reactive power equations using constant
N. D. Rao, S. C. Tripathy
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Variable Step-Size Sign Subband Adaptive Filter

IEEE Signal Processing Letters, 2013
This letter proposes a variable step-size sign subband adaptive filter (SSAF) based on the minimization of mean-square deviation (MSD). In the process of minimizing the MSD, because it is not feasible to know the exact value of the MSD, the step size is derived by minimizing the upper bound of the MSD in each iteration. The proposed algorithm uses this
Shin, J, Yoo, J, Park, P
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