Results 251 to 260 of about 1,639,808 (340)
Microbiome assembly statistics toward ecosystem-scale insights, forecasting, and management. [PDF]
Toju H +18 more
europepmc +1 more source
Ecological drift simulations reveal key factors influencing minimal microbiome engineering and community assembly. [PDF]
Talavera-Marcos S, Aguirre de Cárcer D.
europepmc +1 more source
Succession in a Tropical Dry Forest: A Test of the Chronosequence and Inference of Community Assembly Dynamics. [PDF]
Carrington ME, Ross MS, Subedi SC.
europepmc +1 more source
Disturbance observer-based disturbance attenuation control for a class of stochastic systems
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Xinjiang Wei, Hamid Reza Karimi
exaly +3 more sources
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Shixiang Sun +4 more
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International Journal of Robust and Nonlinear Control, 2019
SummaryAntidisturbance control problem is discussed for stochastic systems with multiple heterogeneous disturbances, which include the white noise and the disturbance with unknown frequencies and amplitudes. An adaptive disturbance observer is designed to estimate the disturbance with unknown frequencies and amplitudes, based on which, an adaptive ...
Xinjiang Wei, Lewei Dong, Xin Hu
exaly +3 more sources
SummaryAntidisturbance control problem is discussed for stochastic systems with multiple heterogeneous disturbances, which include the white noise and the disturbance with unknown frequencies and amplitudes. An adaptive disturbance observer is designed to estimate the disturbance with unknown frequencies and amplitudes, based on which, an adaptive ...
Xinjiang Wei, Lewei Dong, Xin Hu
exaly +3 more sources
IEEE Transactions on Aerospace and Electronic Systems, 2023
In this article, the stochastic anti-disturbance control issue is studied for helicopter systems under multiple random disturbances, in which part disturbances are induced by random noises and others are described by Markovian jump models.
Yankai Li, Mou Chen, Peng Shi
exaly +2 more sources
In this article, the stochastic anti-disturbance control issue is studied for helicopter systems under multiple random disturbances, in which part disturbances are induced by random noises and others are described by Markovian jump models.
Yankai Li, Mou Chen, Peng Shi
exaly +2 more sources
Stochastic regression model with heteroscedastic disturbance
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chang, Der-Shin, Lin, Guan-Chyun
core +4 more sources

