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HDM can be used in different manners: as a modeling device or as an implementation device.
As a modeling device, it supports producing high level specifications of existing or to-be-developed applications.
After training, the general Neuro-SM model with appropriate hidden neurons and delay buffers can accurately represent the nonlinear behavior of the modeling device.
I D, S D, and HB D represent the DC current, small-signal S parameter, and large-signal HB responses of the modeling device, respectively.
This feature is also a useful modeling device.
Targeted applications include display device modeling, device independent color reproduction, colorimetry instrumentation, virtual reality, and color machine vision applications.
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Emphasis is put on modeling devices that may simplify the structure or speed up the calculations, which in real world models are of vast dimensions.
It is found that rigorous electrodynamic models that are not based on homogenization of composites can be effectively used in modeling devices with metamaterials.
It deals with a broad set of design issues (e.g., bill-of-material restrictions, international financial considerations, and material flow through the entire supply chain) using effective modeling devices (e.g., linearizing non-linearities that arise in modeling transfer prices and allocating transportation charges).
However, when the modeling devices have both more nonlinearity and high order dynamics, in such case, even though existing Neuro-SM [13, 14] is used to map the coarse model towards the device data, the match between the trained Neuro-SM models and the device data may be still not good enough.
In this study, we investigated experimentally a practical version of the bipolar electrostatic ionizer with a modeling test device to measure the charge-neutralizing current, and a full-sized pneumatic powder transport facility.
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