Results 241 to 250 of about 114,557 (285)

Covariance Matrix Adaptation for Multi-objective Optimization

Evolutionary Computation, 2007
The covariancematrix adaptation evolution strategy (CMA-ES) is one of themost powerful evolutionary algorithms for real-valued single-objective optimization. In this paper, we develop a variant of the CMA-ES for multi-objective optimization (MOO). We first introduce a single-objective, elitist CMA-ES using plus-selection and step size control based on
Christian, Igel, Nikolaus, Hansen, Stefan, Roth   +2 more
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An adaptive penalty based covariance matrix adaptation–evolution strategy

Computers & Operations Research, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kusakci, Ali Osman, Can, Mehmet
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Simplified covariance matrix measurement in adaptive arrays

IEEE Transactions on Aerospace and Electronic Systems, 1990
A procedure to measure the covariance matrix of an antenna array directly from the array sum port is described. The procedure involves specifically chosen sets of antenna weights that will allow the matrix components to appear at the array output. A parallel method of measuring the covariance matrix is also described.
D.J. Farina, R.P. Flam
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Enhanced Covariance Matrix Estimators in Adaptive Beamforming

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
In this paper a number of covariance matrix estimators suggested in the literature are compared in terms of their performance in the context of array signal processing. More specifically they are applied in adaptive beamforming which is known to be sensitive to errors in the covariance matrix estimate and where often only a limited amount of data is ...
Richard Abrahamsson, Yngve Selen, Petre Stoica   +2 more
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Adaptive covariance matrix for object region representation

SPIE Proceedings, 2013
Region covariance descriptor has been employed in many computer vision applications such as texture classification, object detection and object tracking. It provides a natural way of fusing multiple features based on a set of pixels of a given region.
Lei Qin, Hichem Snoussi, Fahed Abdallah
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Covariance Matrix Adaptation for Multiobjective Multiarmed Bandits

IEEE Transactions on Neural Networks and Learning Systems, 2019
Upper confidence bound (UCB) is a successful multiarmed bandit for regret minimization. The covariance matrix adaptation (CMA) for Pareto UCB (CMA-PUCB) algorithm considers stochastic reward vectors with correlated objectives. We upper bound the cumulative pseudoregret of pulling suboptimal arms for the CMA-PUCB algorithm to logarithmic number of arms ...
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Band-Inverse TVAR Covariance Matrix Estimation for Adaptive Detection

IEEE Transactions on Aerospace and Electronic Systems, 2010
We characterize a sequence of M interference observations by a time-varying autoregressive model of order m (TVAR(m)). We recently demonstrated that the maximum-likelihood (ML) TVAR(m) covariance matrix estimate (CME) of Gaussian data is the Dym-Gohberg transformation of the sample (direct data) covariance matrix averaged over the T independent ...
Abramovich, Yuri, Spencer, Nicholas K., Johnson, Ben A.   +2 more
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Improving the Ensemble-Optimization Method Through Covariance-Matrix Adaptation

SPE Journal, 2014
Summary Ensemble optimization (referred to throughout the remainder of the paper as EnOpt) is a rapidly emerging method for reservoir-model-based production optimization. EnOpt uses an ensemble of controls to approximate the gradient of the objective function with respect to the controls.
Fonseca, R.M., Leeuwenburgh, O., Hof, P.M.J. van den, Jansen, J.D.   +3 more
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Robust adaptive beamforming using interference covariance matrix reconstruction

2016 CIE International Conference on Radar (RADAR), 2016
The performance of adaptive beamforming degrades severely when the strong desired signal is present in training snapshots with model mismatch. A robust adaptive beamforming is proposed using interference covariance matrix reconstruction in this paper. In the proposed method, the eigenvalue and eigenvector of desired signal is determined by calculating ...
Xueyao Hu, Teng Yu, Xinyu Zhang, Yanhua Wang, Hongyu Wang, Yang Li   +5 more
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Adaptive Beamforming Based on Interference Covariance Matrix Estimation

2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019
In this paper, we propose a robust adaptive beam-forming algorithm, where the interference-plus-noise covariance matrix is estimated by identifying and removing the desired signal component from the sample covariance matrix. For this purpose, we construct a desired signal subspace and its orthogonal subspace to identify the eigenvector of the sample ...
Yujie Gu, Yimin D. Zhang
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