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ON THE IMAGES OF LM-G-FILTERS AND LM-G-FILTERBASES
South East Asian J. of Mathematics and Mathematical Sciences, 2022This paper studies LM-G-filters as a generalization of LM-filters. Images of LM-G-filter spaces and LM-G-filterbases induced by functions are investigated and some of their properties are derived. It is shown that the property of being weakly inspired, catalyzed, s-stratified and stratification of LM-G-filter spaces are preserved by images.
Jose, Merin, Mathew, Sunil C.
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2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
Considering groups of variables, rather than variables individually, can be beneficial for estimation accuracy if structural relationships between variables exist (e.g., spatial, hierarchical or related to the physics of the problem). Group-sparsity inducing estimators are typical examples that benefit from such type of prior knowledge.
Chen, Jie +3 more
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Considering groups of variables, rather than variables individually, can be beneficial for estimation accuracy if structural relationships between variables exist (e.g., spatial, hierarchical or related to the physics of the problem). Group-sparsity inducing estimators are typical examples that benefit from such type of prior knowledge.
Chen, Jie +3 more
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What Was Lost with IS-LM [PDF]
History of Political Economy, 2004 The dominance of the IS-LM model in macroeconomics after 1937 led to the neglect and sometimes the outright loss of a number of important issues that had earlier been prominent in the literature. All these losses were related to the fact that economic life takes place over time, from which the IS-LM model's formal comparative static nature abstracted ...
Roger Backhouse, David Laidler
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IEEE Transactions on Signal Processing, 1997
New and weak conditions are given under which the LMS algorithm is exponentially convergent with probability one in a stochastic setting. These results show that LMS works under very broad conditions: a small gain, input signal with finite fourth moments; time varying persistence of excitation; and weak assumptions on the correlation structure of the ...
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New and weak conditions are given under which the LMS algorithm is exponentially convergent with probability one in a stochastic setting. These results show that LMS works under very broad conditions: a small gain, input signal with finite fourth moments; time varying persistence of excitation; and weak assumptions on the correlation structure of the ...
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On the convergence behavior of the LMS and the normalized LMS algorithms
IEEE Transactions on Signal Processing, 1993It is shown that the normalized least mean square (NLMS) algorithm is a potentially faster converging algorithm compared to the LMS algorithm where the design of the adaptive filter is based on the usually quite limited knowledge of its input signal statistics.
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Conversion of the delayed LMS algorithm into the LMS algorithm
IEEE Signal Processing Letters, 1995For some applications of adaptive finite impulse response (FIR) filtering, the adaptation algorithm can be implemented only with a delay in the coefficient update. It is well known that this has an adverse effect on the convergence behavior of the algorithm.
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Convergence of matrix LMS algorithms with applications to LMS/Newton
Signal Processing, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
C. Horn, Raymond H. Kwong
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Convex Combination of Transform Domain LMS and Sparse LMS
2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018This work introduces a convex combination of two algorithms, namely, the Transform Domain LMS (TD-LMS) algorithm and its sparse-aware $L_{1}$ version known as the Zero-Attractor Transform Domain LMS (TD-ZA-LMS), to solve the problem of variable sparsity rate under highly correlated input environments. This combination has the ability to converge to the
Naveed Iqbal 0001 +2 more
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Adjoint LMS: an efficient alternative to the filtered-x LMS and multiple error LMS algorithms
1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, 2002The Filtered-x LMS algorithm is currently the most popular method for adapting a filter when there exists a transfer function in the error path. Such instances arise, for example, in active control of sound and vibration. For multiple-input-multiple-output systems the Multiple Error LMS Algorithm is a generalization of Filtered-x LMS. The derivation of
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