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Algorithm for sparse representation minimizing mean square error of power spectrograms
2015 IEEE International Conference on Digital Signal Processing (DSP), 2015Sparse representation is an idea to approximate a target signal by a linear combination of a small number of sample signals, and it is utilized in various research fields. In this paper, we evaluate the approximation error of signals by the mean square error of power spectrograms (P-MSE).
Yuma Tanaka +2 more
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Bias Adjustment Minimizing the Asymptotic Mean Square Error
Communications in Statistics - Theory and Methods, 2013A method of bias adjustment which minimizes the asymptotic mean square error is presented for an estimator typically given by maximum likelihood. Generally, this adjustment includes unknown population values. However, in some examples, the adjustment can be done without population values.
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Steady-state error in adaptive mean-square minimization
IEEE Transactions on Information Theory, 1970This paper considers the steady-state mean-square error when an adaptive linear estimator is used on a stationary time series. The estimator weights are adjusted periodically by moving a small increment in the direction of the estimated gradient. Under very general conditions the asymptotic mean-square error is bounded and under more restrictive ...
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Supermodular mean squared error minimization for sensor scheduling in optimal Kalman Filtering
2017 American Control Conference (ACC), 2017We consider the problem of scheduling a set of sensors to observe the state of a discrete-time linear system subject to a limited energy budget. Our goal is to devise a sensor schedule that minimizes the mean squared error (MSE) of an optimal estimator (i.e., the Kalman Filter). Both the minimum-MSE and the minimum-cardinality optimal sensor scheduling
Prince Singh +5 more
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Minimization of mean-square error for data transmitted via group codes
IEEE Transactions on Information Theory, 1969We show how to find solutions to the problem considered by Mitryayev [l ] in the case where the loss power function is quadratic. This problem is to minimize mean-square error when digital data is represented by group code combinations and the a priori probability distribution is uniform.
Crimmins, T. R. +3 more
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An iterative approach to minimize the mean squared error in ridge regression
Computational Statistics, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wong, Ka Yiu, Chiu, Sung Nok
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2008 10th Brazilian Symposium on Neural Networks, 2008
In this paper, artificial neural networks (ANN) are used to model an extraction process that uses a supercritical fluid as solvent which its pilot installation is located at the Institute of Experimental and Technological Biology - IBET in Oeiras - Lisbon - Portugal.
Rosana Paula de Oliveira Soares +3 more
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In this paper, artificial neural networks (ANN) are used to model an extraction process that uses a supercritical fluid as solvent which its pilot installation is located at the Institute of Experimental and Technological Biology - IBET in Oeiras - Lisbon - Portugal.
Rosana Paula de Oliveira Soares +3 more
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IEEE Transactions on Signal Processing, 2011
In the past, ad hoc methods have been used to choose gains in proportionate-type normalized least mean-square algorithms without strong theoretical under-pinnings. In this correspondence, a theoretical framework and motivation for adaptively choosing gains is presented, such that the mean-square error will be minimized at any given time. As a result of
Kevin Wagner, Milos Doroslovacki
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In the past, ad hoc methods have been used to choose gains in proportionate-type normalized least mean-square algorithms without strong theoretical under-pinnings. In this correspondence, a theoretical framework and motivation for adaptively choosing gains is presented, such that the mean-square error will be minimized at any given time. As a result of
Kevin Wagner, Milos Doroslovacki
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PtNLMS algorithm obtained by minimization of mean square error modeled by exponential functions
2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010Using the proportionate-type steepest descent algorithm we represent the current weight deviations in terms of initial weight deviations. Then we attempt to minimize the mean square output error with respect to the gains at a given instant. The corresponding optimal average gains are found using a water-filling procedure.
Kevin T. Wagner, Milos I. Doroslovacki
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Linear model averaging by minimizing mean-squared forecast error unbiased estimator
Model Assisted Statistics and Applications, 2016This paper presents a new ordinary least squares model averaging method which is proposed to be a preferable alternative to Mallows Model Averaging (MMA), Bayesian Model Averaging (BMA) and naïve simple forecast average. The method is developed to deal with possibly non-nested models and selects forecast weights by minimizing the unbiased estimator of ...
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