Results 21 to 30 of about 24,564,249 (381)

A posteriori error estimates of hp spectral element method for parabolic optimal control problems

open access: yesAIMS Mathematics, 2022
In this paper, we investigate the spectral element approximation for the optimal control problem of parabolic equation, and present a hp spectral element approximation scheme for the parabolic optimal control problem.
Zuliang Lu   +5 more
doaj   +1 more source

Nonlinear System Identification of Neural Systems from Neurophysiological Signals

open access: yesNeuroscience, 2020
The human nervous system is one of the most complicated systems in nature. Complex nonlinear behaviours have been shown from the single neuron level to the system level.
F. He, Yuan Yang
semanticscholar   +1 more source

A priori error estimates of finite volume element method for bilinear parabolic optimal control problem

open access: yesAIMS Mathematics, 2023
In this paper, we study the finite volume element method of bilinear parabolic optimal control problem. We will use the optimize-then-discretize approach to obtain the semi-discrete finite volume element scheme for the optimal control problem. Under some
Zuliang Lu   +3 more
doaj   +1 more source

On the Approximation of Moments for Nonlinear Systems [PDF]

open access: yesIEEE Transactions on Automatic Control, 2021
Model reduction by moment-matching relies upon the availability of the so-called moment . If the system is nonlinear, the computation of moments depends on an underlying specific invariance equation, which can be difficult or impossible to solve. This article presents four technical contributions related to the theory of moment matching: first, we ...
Nicolas Faedo   +3 more
openaire   +7 more sources

Superconvergence for optimal control problems governed by semilinear parabolic equations

open access: yesAIMS Mathematics, 2022
In this paper, we first investigate optimal control problem for semilinear parabolic and introduce the standard $ L^2(\Omega) $-orthogonal projection and the elliptic projection.
Chunjuan Hou   +4 more
doaj   +1 more source

Discovering governing equations from data by sparse identification of nonlinear dynamical systems [PDF]

open access: yesProceedings of the National Academy of Sciences of the United States of America, 2015
Significance Understanding dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled technology, including aircraft, combustion engines, satellites, and electrical power. This work develops a novel framework to
S. Brunton, J. Proctor, J. Kutz
semanticscholar   +1 more source

Practical challenges in data‐driven interpolation: Dealing with noise, enforcing stability, and computing realizations

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary In this contribution, we propose a detailed study of interpolation‐based data‐driven methods that are of relevance in the model reduction and also in the systems and control communities. The data are given by samples of the transfer function of the underlying (unknown) model, that is, we analyze frequency‐response data.
Quirin Aumann, Ion Victor Gosea
wiley   +1 more source

Data‐driven performance metrics for neural network learning

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri   +2 more
wiley   +1 more source

Error estimates of variational discretization for semilinear parabolic optimal control problems

open access: yesAIMS Mathematics, 2021
In this paper, variational discretization directed against the optimal control problem governed by nonlinear parabolic equations with control constraints is studied.
Chunjuan Hou   +3 more
doaj   +1 more source

Identification of Nonlinear Systems Using Radial Basis Function Neural Network [PDF]

open access: yes, 2014
This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are ...
Pislaru, Crinela, Shebani, Amer
core   +1 more source

Home - About - Disclaimer - Privacy