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The study population included those with diseases of the circulatory system, identified using the International Classification of Diseases (ICD), 9th Revision, Clinical Modification diagnosis codes (390-459).
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First based on the input-output data of the system, the Markov parameters of the system are identified using the method of observer/Kalman filter identification (OKID).
For each participant, two bedding systems, one inducing the strongest limb muscle force (strong bedding system) and the other inducing the weakest limb force (weak bedding system), were identified using MMT.
In order to design the controller of nonlinear electrohydraulic system, several modeling techniques are proposed: the transfer function of the electrohydraulic system is identified using first-principle method, and the intelligent models are built by fuzzy modeling and neural networks.
Once the unknown nonlinear system is identified using Laguerre-ELM Wiener model, a generalized predictive control (GPC) algorithm is designed for control of nonlinear system.
The system is identified using a black-box polynomial nonlinear state-space identification procedure.
The transfer function matrix of the system is identified using the prediction error (PE) identification method with multivariable ARX model.
A NARMA model of the inverse of the uncontrolled system is identified using reversed input output data collected experimentally from the plant.
Structural properties of single and mixed systems were identified using small-deformation dynamic oscillation in shear, microdifferential scanning calorimetry and scanning electron microscopy.
Protein localizations were predicted with PSORTb [50] and twin-arginine transystemson systems were identified using TatP program [51].
Sensitivities of parameters in regards to IL-8 level (t = 15,000) in the systems were identified using the MatLab SimBiology module with the Sundial solver.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com