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This paper proposes an intelligent model parameter identification method using particle swarm optimization (PSO).
In this work I present a general algorithm for coupling state variable and model parameter identification.
Model parameter identification and validation is accomplished by comparing numerically simulated and experimentally measured temperatures and outlet product concentrations.
A new explicit approach for the simultaneous online optimal experiment design (OED) and model parameter identification is presented.
The simplified refined instrument variable algorithm was used as a model parameter identification tool to obtain the best model order and parameters.
This paper develops an on-line model parameter identification approach for multivariable systems, which are nonlinear in terms of state representation and/or in terms of parameters.
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Some characterization test results to model parameters identification are included.
Therefore, this paper treats the problems related to building modeling and model parameters identification.
Partially automated regularisation procedure is suggested for the model parameters identification.
This formulation leads to the solution of the modal problem via multi-dimensional ARMA model parameters identification.
The aim of this study is to present the CIP model parameters identification approach based on the information captured by time series of the COP signal.
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model parameter space
model parameter calibration
model parameter optimization
model parameter sensitivity
model parameter determination
model parameter estimation
model parameter m
model parameter description
model parameter variation
model parameter vector
model parameter estimator
model parameter extraction
modal parameter identification
model parameter obtained
model parameter determined
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