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 Parametric Modeling of EM Behavior of Microwave Components Using Combined Neural Networks and Hybrid-Based Transfer Functions

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 93922-93938 ◽  
Author(s):  
Zhihao Zhao ◽  
Feng Feng ◽  
Wei Zhang ◽  
Jianan Zhang ◽  
Jing Jin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Functions ◽  
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

Fourier Series-Based Neural Networks for Vehicle System Identification

1999 ◽  
Author(s):  
Imtiaz Haque ◽  
Juergen Schuller
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
System Identification ◽  
Transfer Function ◽  
Parameter Identification ◽  
Frequency Domain ◽  
Vehicle System ◽  
Non Linear ◽  
Function Identification ◽  
Non Linear System

Abstract The use of neural networks in system identification is an emerging field. Neural networks have become popular in recent years as a means to identify linear and non-linear systems whose characteristics are unknown. The success of sigmoidal networks in parameter identification has been limited. However, harmonic activation-based neural networks, recent arrivals in the field of neural networks, have shown excellent promise in linear and non-linear system parameter identification. They have been shown to have excellent generalization capability, computational parallelism, absence of local minima, and good convergence properties. They can be used in the time and frequency domain. This paper presents the application of a special class of such networks, namely Fourier Series neural networks (FSNN) to vehicle system identification. In this paper, the applications of the FSNNs are limited to the frequency domain. Two examples are presented. The results of the identification are based on simulation data. The first example demonstrates the transfer function identification of a two-degree-of freedom lateral dynamics model of an automobile. The second example involves transfer function identification for a quarter car model. The network set-up for such identification is described. The results of the network identification are compared with theory. The results indicate excellent prediction properties of such networks.

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