Results 241 to 250 of about 269,153 (284)

Prediction of estimated glomerular filtration rate slope and kidney prognosis of patients with chronic kidney disease. [PDF]

open access: yesSci Rep
Nagasu H   +8 more
europepmc   +1 more source

Accuracy of glomerular filtration rate estimation based on creatinine and cystatin C for monitoring moderate chronic kidney disease in adults: prospective, longitudinal cohort study.

open access: yesBMJ
Scandrett K   +21 more
europepmc   +1 more source

Semidefinite programming for uncertain linear equations in static analysis of structures

open access: yesSemidefinite programming for uncertain linear equations in static analysis of structures
openaire  

Earth’s inner core composition inferred from the equations of state of Fe-Si-C-H alloys

open access: yes
Kolesnikov E   +10 more
europepmc   +1 more source

Ellipsoidal bounds for uncertain linear equations and dynamical systems

Automatica, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
CALAFIORE, Giuseppe Carlo, EL GHAOUI L.
openaire   +5 more sources

Semidefinite programming for uncertain linear equations in static analysis of structures

Computer Methods in Applied Mechanics and Engineering, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kanno, Yoshihiro, Takewaki, Izuru
openaire   +4 more sources

A riccati equation approach to the stabilization of uncertain linear systems

Automatica, 1986
The paper deals with the problem of designing a controller when no accurate model is available for the process to be controlled. Specifically, the problem of stabilizing an uncertain system using state feedback control is considered. The unknown parameters are assumed to be bounded and to vary within time.
PETERSEN, IR, Hollot, CV
openaire   +5 more sources

A Riccati equation approach to the design of stabilizing controllers and observers for a class of uncertain linear systems

IEEE Transactions on Automatic Control, 1985
Consider uncertain linear systems (1a) \(\dot x(t)=[A_ 0+\sum^{k}_{i=1}A_ ir_ i(t)]x(t)+[B_ 0+\sum^{\ell}_{i=1}B_ is_ i(t)]u(t)\), (1b) \(y(t)=[C_ 0+\sum^{p}_{i=1}C_ iv_ i(t)]x(t)\) where \(x(t)\in R^ n\) is the state, \(u(t)\in R^ m\) is the control, \(y(t)\in R^ q\) is the measured output, \(r_ i(t)\), \(s_ i(t)\), \(v_ i(t)\) are uncertain ...
Ian Petersen
openaire   +3 more sources

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