Estimation and Application of Mean Square Frequency
نویسندگان
چکیده
منابع مشابه
Mean Square Estimation
The problem of parameter estimation in linear model is pervasive in signal processing and communication applications. It is often common to restrict attention to linear estimators, which simplifies the implementation as well as the mathematical derivations. The simplest design scenario is when the second order statistics of the parameters to be estimated are known and it is desirable to minimiz...
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Neural networks for estimation, such as the multilayer perceptron (MLP) and functional link net (FLN), are shown to approximate the minimum mean square estimator rather than the maximum likelihood estimator or others. Cramer-Rao maximum a posteriori lower bounds on estimation error can therefore be used to approximately bound network training error, when a statistical signal model is available ...
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Estimation of signals with nonlinear as well as linear parameters in noise is studied. Maximum likelihood estimation has been shown to perform the best among all the methods. In such problems, joint maximum likelihood estimation of the unknown parameters reduces to a separable optimization problem, where first, the nonlinear parameters are estimated via a grid search, and then, the nonlinear pa...
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Least mean square (LMS)-based adaptive algorithms have attracted much attention due to their low computational complexity and reliable recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods have been proposed based on different sparse penalties, such as l1-norm LMS or zeroattracting LMS (ZA-LMS), reweighted ZA-LMS, and lp-norm LMS. However, th...
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ژورنال
عنوان ژورنال: Journal of the Japan Society of Precision Engineering
سال: 1979
ISSN: 0374-3543
DOI: 10.2493/jjspe1933.45.1321