Recent advances in parametric modeling of microwave components using combined neural network and transfer function

2020 ◽  
Vol 33 (6) ◽  
Author(s):  
Feng Feng ◽  
Jianan Zhang ◽  
Wei Zhang ◽  
Zhihao Zhao ◽  
Jing Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Parametric Modeling ◽  
Recent Advances ◽  
Microwave Components

scholarly journals Multiphysics Parametric Modeling of Microwave Components Using Combined Neural Networks and Transfer Function

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 5383-5392
Author(s):  
Wei Zhang ◽  
Feng Feng ◽  
Shuxia Yan ◽  
Zhihao Zhao ◽  
Weicong Na
Keyword(s):  
Neural Networks ◽  
Transfer Function ◽  
Parametric Modeling ◽  
Microwave Components

scholarly journals A Novel Deep Neural Network Topology for Parametric Modeling of Passive Microwave Components

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 82273-82285 ◽  
Author(s):  
Jing Jin ◽  
Feng Feng ◽  
Jianan Zhang ◽  
Shuxia Yan ◽  
Weicong Na ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Topology ◽  
Deep Neural Network ◽  
Parametric Modeling ◽  
Passive Microwave ◽  
Microwave Components ◽  
Passive Microwave Components

scholarly journals Automated Neural Network-Based Multiphysics Parametric Modeling of Microwave Components

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 141153-141160
Author(s):  
Weicong Na ◽  
Wanrong Zhang ◽  
Shuxia Yan ◽  
Feng Feng ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Parametric Modeling ◽  
Microwave Components

scholarly journals Novel Decomposition Technique on Rational-Based Neuro-Transfer Function for Modeling of Microwave Components

Micromachines ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 696
Author(s):  
Zhihao Zhao ◽  
Feng Feng ◽  
Jianan Zhang ◽  
Wei Zhang ◽  
Jing Jin ◽  
...  
Keyword(s):  
Transfer Function ◽  
Lower Order ◽  
High Sensitivity ◽  
Parametric Modeling ◽  
Model Output ◽  
Decomposition Technique ◽  
Popular Method ◽  
Model Response ◽  
Microwave Components ◽  
Modeling Accuracy

The rational-based neuro-transfer function (neuro-TF) method is a popular method for parametric modeling of electromagnetic (EM) behavior of microwave components. However, when the order in the neuro-TF becomes high, the sensitivities of the model response with respect to the coefficients of the transfer function become high. Due to this high-sensitivity issue, small training errors in the coefficients of the transfer function will result in large errors in the model output, leading to the difficulty in training of the neuro-TF model. This paper proposes a new decomposition technique to address this high-sensitivity issue. In the proposed technique, we decompose the original neuro-TF model with high order of transfer function into multiple sub-neuro-TF models with much lower order of transfer function. We then reformulate the overall model as the combination of the sub-neuro-TF models. New formulations are derived to determine the number of sub-models and the order of transfer function for each sub-model. Using the proposed decomposition technique, we can decrease the sensitivities of the overall model response with respect to the coefficients of the transfer function in each sub-model. Therefore, the modeling approach using the proposed decomposition technique can increase the modeling accuracy. Two EM parametric modeling examples are used to demonstrate the proposed decomposition technique.

Space Mapping Approach to Electromagnetic Centric Multiphysics Parametric Modeling of Microwave Components

2018 ◽  
Vol 66 (7) ◽  
pp. 3169-3185 ◽  
Author(s):  
Wei Zhang ◽  
Feng Feng ◽  
Venu-Madhav-Reddy Gongal-Reddy ◽  
Jianan Zhang ◽  
Shuxia Yan ◽  
...  
Keyword(s):  
Parametric Modeling ◽  
Space Mapping ◽  
Mapping Approach ◽  
Microwave Components

Optimization of the Robots Fourier Spectrum by Using the Assisted Research, Neural Network, Smart Damper and LabVIEW Instrumentation

2012 ◽  
Vol 245 ◽  
pp. 24-32 ◽  
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Aurel Oprean
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Spectral Density ◽  
Vibration Analysis ◽  
Manufacturing Systems ◽  
Fourier Spectrum ◽  
Industrial Robots ◽  
Vibration Behavior ◽  
Power Spectral ◽  
The World

The most important things in the dynamic research of industrial robots are the vibration behavior, the transfer function and the vibration power spectral density between some of the robot joints and components. In the world this research is made without the assisted research. In each of the study cases in this paper was used the proper virtual Fourier analyzer and was presented one new method of the assisted vibration analysis. With this research it is possible the optimal choosing the base modulus type to avoid the frequencies from the robot spectrum. In the manufacturing systems, the most important facts are the vibration behavior of the robot, the compatibility with some other components of the system. All the VI where achieved in the LabVIEW soft 8.2 version, from National Instruments, USA. This method and the created virtual LabVIEW instrumentation are generally and they are possible to apply in many other dynamic behavior research.

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