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Identification of systems with slowly sampled output is studied.
This paper studies identification of systems in which only quantized output observations are available.
In this paper, identification of systems with binary-valued observations is investigated.
Mathematical modelling and identification of systems, where the structure of some equations is unknown, is a difficult task.
We consider identification of systems with a parallel serial (cascade) structure with multiple-input and multiple-output signals.
The experimental identification of systems in structural dynamics is commonly achieved by adapting a parametric model so that its simulated response matches a set of measurements.
Similar(44)
Authentication is the secure identification of system users.
Identification of system matrices is made in the algebraic form.
A plethora of algorithms and theories developed in the field of Machine Learning enable better identification of system dynamics and extensive control of the corresponding systems.
The obtained results showed that the proposed method could act as an effective model in identification of system properties.
Closed-loop identification of system parameters and control gains using system classification-based neural networks is proposed and implemented.
More suggestions(17)
identification of schemes
evidence of systems
identification of models
characterization of systems
requirements of systems
identification of mechanism
identification of hardware
identification of health
identification of device
identification of systematic
identification of vehicles
identification of mutations
identification of pathogens
identification of vessels
identification of cohorts
identification of genes
identification of studies
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