T–S Fuzzy Model Identification with Sparse Bayesian Techniques
Sign Up to like & getrecommendations! Published in 2019 at "Neural Processing Letters"
DOI: 10.1007/s11063-019-10071-3
Abstract: This paper introduces a novel method for fuzzy modeling based on sparse Bayesian techniques. The sparse representation problems in the Takagi–Sugeno (T–S) fuzzy system identification are studied, which is to establish a T–S fuzzy system… read more here.
Keywords: fuzzy; fuzzy system; bayesian techniques; model ... See more keywords
Sparse Bayesian learning approach for baseline correction
Sign Up to like & getrecommendations! Published in 2020 at "Chemometrics and Intelligent Laboratory Systems"
DOI: 10.1016/j.chemolab.2020.104088
Abstract: Abstract Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction… read more here.
Keywords: baseline correction; correction; sparse bayesian; performance ... See more keywords
An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework
Sign Up to like & getrecommendations! Published in 2019 at "Computer Methods in Applied Mechanics and Engineering"
DOI: 10.1016/j.cma.2019.04.046
Abstract: Abstract Sparse polynomial chaos expansion has been widely used to tackle problems of function approximation in the field of uncertain quantification. The accuracy of PCE depends on how to construct the experimental design. Therefore, adaptive… read more here.
Keywords: chaos expansion; polynomial chaos; sparse bayesian; adaptive sampling ... See more keywords
Sparse Bayesian time-varying covariance estimation in many dimensions
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Econometrics"
DOI: 10.1016/j.jeconom.2018.11.007
Abstract: Dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality. This renders parsimonious estimation methods essential for conducting reliable statistical inference. In this paper, the issue is addressed by modeling the underlying… read more here.
Keywords: estimation; time varying; covariance estimation; time ... See more keywords
Sparse Bayesian learning for structural damage identification
Sign Up to like & getrecommendations! Published in 2020 at "Mechanical Systems and Signal Processing"
DOI: 10.1016/j.ymssp.2020.106689
Abstract: Abstract Identification of structural parameters can be cast as the process of solving an inverse problem, in which regularization may be required when the problem is ill-posed. Bayesian inference provides a probabilistic interpretation of the… read more here.
Keywords: structural damage; damage identification; sparse bayesian; identification ... See more keywords
Sparse Bayesian learning for spinning antenna DOA super-resolution
Sign Up to like & getrecommendations! Published in 2018 at "Electronics Letters"
DOI: 10.1049/el.2017.4010
Abstract: The spinning, wide bandwidth antenna remains the most cost-effective technique for finding the direction of arrival of emitters’ signals. The beam is broadest at the low edge of the monitored band, resulting in poor angular… read more here.
Keywords: resolution; spinning antenna; super resolution; sparse bayesian ... See more keywords
Sparse Bayesian mass-mapping with uncertainties: Full sky observations on the celestial sphere
Sign Up to like & getrecommendations! Published in 2020 at "Monthly Notices of the Royal Astronomical Society"
DOI: 10.1093/mnras/staa3563
Abstract: To date weak gravitational lensing surveys have typically been restricted to small fields of view, such that the flat-sky approximation has been sufficiently satisfied. However, with Stage IV surveys (e.g. LSST and Euclid) imminent, extending… read more here.
Keywords: bayesian mass; mass mapping; mass; sparse bayesian ... See more keywords
Anisotropic interaction rules in circular motions of pigeon flocks: An empirical study based on sparse Bayesian learning.
Sign Up to like & getrecommendations! Published in 2017 at "Physical Review E"
DOI: 10.1103/physreve.96.022411
Abstract: Coordination shall be deemed to the result of interindividual interaction among natural gregarious animal groups. However, revealing the underlying interaction rules and decision-making strategies governing highly coordinated motion in bird flocks is still a long-standing… read more here.
Keywords: pigeon flocks; sparse bayesian; interaction rules; interaction ... See more keywords
Theoretical and Experimental Comparison of Off-Grid Sparse Bayesian Direction-of-Arrival Estimation Algorithms
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Access"
DOI: 10.1109/access.2017.2747153
Abstract: Off-grid sparse Bayesian learning algorithms for estimating the directions-of-arrival (DOAs) of multiple signals using an array of sensors are attractive in practice due to three primary reasons. First, these algorithms are fully automatic Bayesian algorithms… read more here.
Keywords: grid sparse; ogsbl algorithm; sparse bayesian; estimation algorithms ... See more keywords
Sparse Bayesian Learning-Based Space-Time Adaptive Processing With Off-Grid Self-Calibration for Airborne Radar
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2866497
Abstract: Space–time adaptive processing (STAP) for airborne radar has recently been enriched owing to the development of methods based on sparse recovery techniques. These methods have shown advantages over the conventional ones. However, there are still… read more here.
Keywords: space time; adaptive processing; sparse bayesian; bayesian learning ... See more keywords
An AMP-Based Low Complexity Generalized Sparse Bayesian Learning Algorithm
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2018.2890146
Abstract: In this paper, an approximate message passing-based generalized sparse Bayesian learning (AMP-Gr-SBL) algorithm is proposed to reduce the computation complexity of the Gr-SBL algorithm, meanwhile improving the robustness of the GAMP algorithm against the measurement… read more here.
Keywords: generalized sparse; sparse bayesian; sbl algorithm; algorithm ... See more keywords