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.

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

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

scholarly journals Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling

2013 ◽  
Vol 17 (2) ◽  
pp. 461-478 ◽  
Author(s):  
L. Loosvelt ◽  
H. Vernieuwe ◽  
V. R. N. Pauwels ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Sensitivity Analysis ◽  
Soil Texture ◽  
Compositional Data ◽  
High Sensitivity ◽  
Hydrologic Model ◽  
Model Output ◽  
Local Sensitivity ◽  
Hydrologic Modelling ◽  
Compositional Model ◽  
Additional Data Collection

Abstract. Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moistureretention curve.

Lower-Order Active Disturbance Rejection Control and Frequency Analysis of High Order System

2017 ◽  
Author(s):  
Ruiqing Zhang ◽  
Shubo Zhang ◽  
Yali Xue ◽  
Yu Hu ◽  
Wendi Wang
Keyword(s):  
Transfer Function ◽  
Frequency Analysis ◽  
Lower Order ◽  
Disturbance Rejection ◽  
High Order ◽  
Order System ◽  
Active Disturbance Rejection Control ◽  
High Order System ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

Linear active disturbance rejection control (LADRC) has been paid much attention in academic and industrial fields. However, the selection of the order for LADRC controller design and the choice of parameter b0 in system correcting are key facts which are still being faced by designer. In this paper, an effective method about how to select the order of LADRC and the parameter b0 is given first. Frequency analysis often used by engineers for designing controller, the normal transfer function form of LADRC is constructed, so the loop gain, close loop transfer function and disturbance transfer function to a general high-order system are presented and can be easily used. The example shows that the proposed method is easy to apply and verified the lower-order LADRC can obtain the better effective than PID and high-order LADRC, and the frequency response analysis of a thermal power plant is elaborated and the simulation result indicates that LADRC has a strong robustness against the large variation of parameters in the plant mode.

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

Modeling and Design Implications of Noncollocated Control in Flexible Systems

1990 ◽  
Vol 112 (2) ◽  
pp. 186-193 ◽  
Author(s):  
V. A. Spector ◽  
H. Flashner
Keyword(s):  
Transfer Function ◽  
Structural Control ◽  
High Sensitivity ◽  
Wave Number ◽  
Model Parameters ◽  
Generic Properties ◽  
Distributed Sensors ◽  
Function Zeros ◽  
Euler Bernoulli Beam ◽  
Qualitative Characteristics

In this paper we investigate generic properties of structural modeling pertinent to structural control, with emphasis on noncollocated systems. Analysis is performed on a representative example of a pinned-free Euler-Bernoulli beam with distributed sensors. Analysis in the wave number plane highlights the crucial qualitative characteristics common to all structural systems. High sensitivity of the transfer function zeros to errors in model parameters and sensor locations is demonstrated. The existence of finite right half plane zeros in noncollocated systems, along with this high sensitivity, further complicates noncollocated controls design. A numerical method for accurate computation of the transfer function zeros is proposed. Wiener-Hopf factorization is used to compute equivalent delay time, which is important in controls design.

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