Sentence examples for modeling nonlinear devices from inspiring English sources

Compared to the Neuro-SM presented in [10], Neuro-SM with the output mapping is more suitable for modeling nonlinear devices with more nonlinearity due to the additional and useful degrees of freedom from the output mapping neural network.

Compared to [13] where only static mapping is used, the proposed technique is more suitable for modeling nonlinear devices with higher order dynamic effects and non-quasi-static effect that may be missing in the coarse model due to inclusion of dynamic mapping.

In order to accurately model nonlinear devices which have higher order dynamic effects (e.g., capacitive effect or non-quasi-static effect) than that of the coarse model, dynamic Neuro-SM was introduced [14].

Academic harmonic analysis study consists of modeling nonlinear loads to develop Norton and Thevenin equivalent circuits of devices for integration into harmonic analysis software.

The results support, ANN as the preeminent tool for modelling of nonlinear devices such as memristor and the suite of other emerging nanoelectronics devices.

As seen from Eq. (1), voltages at gate and drain of the coarse model depend on not only the present voltages of the fine model, but also their history signals making the proposed technique more suitable for modeling the dynamic behavior of the nonlinear devices.

This paper has presented a general Neuro-SM technique for nonlinear device modeling.

For nonlinear devices the versatile Bouc Wen model is adopted, whereas for seismic excitation a Gaussian zero mean white noise and a filtered white noise are used.

Therefore, the band-pass PA model can be very accurate to analyze a variety of components in the nonlinear devices, including harmonics, intermodulation, etc.

The specific benefits of these devices in reducing displacement, base shear, or both have been illustrated using linear and experimentally validated nonlinear device models, as well as in a range of large to full scale experiments.

Before the nonlinear device data from simulation or measurement is used for formal training, the general Neuro-SM model is first initialized to be equal to the original coarse model.