Results 11 to 20 of about 2,621,724 (318)

System identification—A survey [PDF]

open access: yesAutomatica, 1971
The field of identification and process-parameter estimation has developed rapidly during the past decade. In this survey paper the state-of-the-art/science is presented in a systematic way. Attention is paid to general properties and to classification of identification problems. Model structures are discussed; their choice hinges on the purpose of the
Åström, Karl Johan, Eykhoff, Pieter
openaire   +2 more sources

Quantum System Identification [PDF]

open access: yesPhysical Review Letters, 2012
The aim of quantum system identification is to estimate the ingredients inside a black box, in which some quantum-mechanical unitary process takes place, by just looking at its input-output behavior. Here we establish a basic and general framework for quantum system identification, that allows us to classify how much knowledge about the quantum system ...
Burgarth, Daniel, Yuasa, Kazuya
openaire   +3 more sources

Blind system identification [PDF]

open access: yesProceedings of the IEEE, 1997
Blind system identification (BSI) is a fundamental signal processing technology aimed at retrieving a system's unknown information from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation, and blind image restoration.
Karim Abed-Meraim   +2 more
openaire   +1 more source

Perspectives on System Identification [PDF]

open access: yesIFAC Proceedings Volumes, 2008
System identification is the art and science of building mathematical models of dynamic systems from observed input-output data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications.
openaire   +1 more source

Accuracy analysis of a covariance matching method for continuous-time errors-in-variables system identification [PDF]

open access: yes, 2012
A covariance matching method for continuous-time errors-in-variables system identification from discrete-time data is analyzed. The asymptotic normalized covariance matrix, valid for a large number of data and a small sampling interval, is evaluated ...
Irshad, Yasir   +7 more
core   +1 more source

AUGMENTING THE PHILIPPINES’ DOST-ASTI’S POTENTIAL FLOOD EXTENTS MAPPING SERVICE WITH S-BAND NOVASAR-1 IMAGES [PDF]

open access: yesThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
The Philippines’ Advanced Science and Technology Institute under the Department of Science and Technology (DOST-ASTI) has developed an AI-based and near real-time flood extent mapping service that utilizes C-Band Sentinel-1 SAR images.
J. B. L. C. Dumalag   +13 more
doaj   +1 more source

Dual Adaptive Model Predictive Controller Application to Vertical Roller Mill Process Used in the Cement Industry

open access: yesIEEE Access, 2020
Diversified operating conditions, input-output constraints, and parametric variations in the Vertical Roller Mill (VRM) make it to have complicated dynamics and closed-loop instability. Existing traditional controllers are not superlative and may lead to
B. Vijayabhaskar, S. Jayalalitha
doaj   +1 more source

Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model

open access: yesApplied Sciences, 2022
The performance of analog-to-digital converters (ADCs) has reached a bottleneck due to the limitations of the manufacturing process and testing environment.
Ning Sun   +8 more
doaj   +1 more source

Biometric identification systems

open access: yesSignal Processing, 2003
2539
Rodrigo de Luis García   +3 more
openaire   +2 more sources

Nonparametric algorithms for identification of nonlinear autoregressive systems with exogenous inputs [PDF]

open access: yes, 2009
This paper is concerned with nonparametric identification of nonlinear autoregressive systems with exogenous inputs (NARX), i.e., $y_{k+1}=f(y_k,cdots,y_{k+1 n_0},u_k,cdots,u_{k+1-n_0})+varepsilon_{k+1}$.
Wen-Xiao Zhao   +3 more
core   +1 more source

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