Results 111 to 120 of about 7,261 (147)

The orbitofrontal cortex forms a context-generalized spatial schema that preserves topology and distance

Basu R, Mozaffarilegha M, Bölükbaşı I, Üstüner C, Ito HT.   +4 more
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From Atoms to Dynamics: Learning the Committor Without Collective Variables

Chipot C, Arredondo SC, Tang C, Talmazan R, Megías A, Chen CG.   +5 more
europepmc   +1 more source

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System Identification Based on Invariant Subspace

IEEE Transactions on Automatic Control, 2022
This paper proposes a novel system identification method based on the notion of invariant subspace. It is shown that when the system input and output asymptotically converge onto an invariant subspace, a new form of regression can be obtained. New identification algorithms are then developed based on the obtained regression.
Chao Huang, Gang Feng, Hao Zhang, Zhuping Wang   +3 more
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Closed loop subspace system identification

Proceedings of the 36th IEEE Conference on Decision and Control, 2002
We present a general framework for closed loop subspace system identification. This framework consists of two new projection theorems which allow the extraction of non-steady state Kalman filter states and of system related matrices directly from input output data.
P. Van Overschee, B. De Moor
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On-line subspace system identification

Control Engineering Practice, 1993
Abstract Although state space identification techniques offer some unique advantages over traditional system identification methods based on input/output transfer functions, the computational burden of state space subspace identification has prevented its real-time application. The major costs result from the need for the singular value (or sometimes
Y.M. Cho, G. Xu, T. Kailath
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Subspace identification of circulant systems

Automatica, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Massioni, Paolo, Verhaegen, Michel
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Continuous-time frequency domain subspace system identification

Signal Processing, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Van Overschee, Peter, De Moor, Bart
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Unbiased bilinear subspace system identification methods

2001 European Control Conference (ECC), 2001
Several subspace algorithms for the identification of bilinear systems have been proposed recently. A key practical problem with all of these is the very large size of the data-based matrices which must be constructed in order to ‘linearise’ the problem and allow parameter estimation essentially by regression.
Huixin Chen, Jan Maciejowski, Chris Cox
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Subspace identification of distributed, decomposable systems

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009
This article concerns the identification of a class of linear systems which we call “decomposable systems”. Such systems can be thought of as the interconnection of a number of identical subsystems, and they can be used to model a number of large scale systems.
Paolo Massioni, Michel Verhaegen
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Subspace Identification of Spatially Distributed Systems*

IFAC Proceedings Volumes, 2011
Abstract We propose a new subspace identification algorithm for identifying a class of spatially varying distributed systems. By exploiting the spatial decay of the distributed system, proposed algorithm identifies the local subsystem dynamics from the local-input output data (input-output data of the subsystems in the vicinity of the local subsystem).
A. Haber, P.R. Fraanje, M. Verhaegen
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