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Convergence Properties in Kalman Filtering

Journal of Information and Optimization Sciences, 1980
The rate of convergence of solutions to dynamical systems is investigated using the Kalman technique. The criterion used is the trace of the error covariance matrix. General and specific formulas are deduced, from which the rate of convergence of solutions to large classes of physical problems may be found.
Donald M. Leskiw, Kenneth S. Miller
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Filter convergence in $����$

2012
We give a necessary combinatorial condition on a filter F to admit an injective F-convergent sequence in $ $. We also show that no analytic filter F admits an injective F-convergent sequence in $ $. This answers a question of T. Banakh, V. Mychaylyuk and L. Zdomskyy.
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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 Kleinberg, Mark Sandler
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Fuzzy Filter Functions and Convergence

1992
In this chapter we describe a fuzzy filter functor in a general framework of set functors. The general theory includes generalized Cauchy spaces, together with a construction for completions, and generalized pseudo-topologies, which in the case of the fuzzy filter functor results in a development of fuzzy convergence structures.
P. Eklund, W. Gähler
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Convergence of an Adaptive Filter with Signed Error Filtering

1988 American Control Conference, 1988
In recent years, 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
Soura Dasgupta   +2 more
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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.
S. Dasgupta, J.S. Garnett, C.R. Johnson
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Local Pre-Hausdorff Constant filter convergence spaces

2018
The aim of this paper is to characterize local pre-Hausdorff constant filter convergence spaces and give some invariance properties of them.
ERCİYES, Ayhan, BARAN, Mehmet
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Dominated convergence and Egorov theorems for filter convergence

2007
Summary: We study the filters, such that for convergence with respect to this filters the Lebesgue dominated convergence theorem and the Egorov theorem on almost uniform convergence are valid (the Lebesgue filters and the Egorov filters, respectively). Some characterizations of the Egorov and the Lebesgue filters are given.
Kadets, V., Leonov, A.
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Closure operators in constant filter convergence spaces

2020
In this paper, we define two notions of closure in the category of constant filter convergence spaces which satisfy productivity, idempotency, and hereditariness. Moreover, by using these closure operators, we characterize each of Ti constant filter convergence spaces, i = 0,1,2 and show that each of these subcategories consisting of Ti constant filter
ERCİYES, Ayhan   +2 more
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Semi-convergence of filters and nets

2016
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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