Neural network of calibrated coarse model and application to substrate integrated waveguide filter design

2020 ◽  
Vol 30 (10) ◽  
Author(s):  
Gong‐Yuan Du ◽  
Long Jin
Keyword(s):  
Neural Network ◽  
Filter Design ◽  
Substrate Integrated Waveguide ◽  
Waveguide Filter ◽  
Coarse Model

Substrate integrated waveguide filter design with wave concept iterative procedure

2011 ◽  
Vol 53 (12) ◽  
pp. 2939-2942 ◽  
Author(s):  
A. Ismail Alhzzoury ◽  
N. Raveu ◽  
G. Prigent ◽  
O. Pigaglio ◽  
H. Baudrand ◽  
...  
Keyword(s):  
Iterative Procedure ◽  
Filter Design ◽  
Substrate Integrated Waveguide ◽  
Waveguide Filter

Control loop oscillation detection and quantification using PRONY method of IIR filter design and deep neural network

2021 ◽  
pp. 1-14
Author(s):  
Sachin Sharma ◽  
Vineet Kumar ◽  
K.P.S. Rana
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Filter Design ◽  
Support Vector ◽  
Control Loop ◽  
External Interference ◽  
Iir Filter ◽  
Prony Method ◽  
Oscillation Detection ◽  
Detection And Quantification

Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later.

scholarly journals Novel Hexagonal Dual-Mode Substrate Integrated Waveguide Filter with Source-Load Coupling

2014 ◽  
Vol 2014 ◽  
pp. 1-5
Author(s):  
Ziqiang Xu ◽  
Gen Zhang ◽  
Hong Xia ◽  
Meijuan Xu
Keyword(s):  
Center Frequency ◽  
Substrate Integrated Waveguide ◽  
Fractional Bandwidth ◽  
Dual Mode ◽  
Transmission Zeros ◽  
Cavity Structure ◽  
Waveguide Filter ◽  
And Performance ◽  
Single Cavity ◽  
Good Agreement

Hexagonal dual-mode cavity and its application to substrate integrated waveguide (SIW) filter are presented. The hexagonal SIW resonator which can combine flexibility of rectangular cavity and performance of circular cavity is convenient for dual-mode bandpass filters design. By introducing coupling between source and load, the filter not only has good selectivity due to two controllable transmission zeros, but also has a small size by the virtue of its single-cavity structure. A demonstration filter with a center frequency of 10 GHz and a 3 dB fractional bandwidth of 4% is designed and fabricated to validate the proposed structure. Measured results are in good agreement with simulated ones.

Novel substrate integrated waveguide filter based on mushroom resonators

2015 ◽  
Author(s):  
Cristiano Tomassoni ◽  
Lorenzo Silvestri ◽  
Maurizio Bozzi ◽  
Luca Perregrini
Keyword(s):  
Substrate Integrated Waveguide ◽  
Waveguide Filter

Artificial Neural Network for the First Filter Design Stage using the Reflected Group Delay Method

2021 ◽  
Author(s):  
Aravindh Reddy Chapala ◽  
Tolulope Ogunleye ◽  
Paul Laforge ◽  
Abdul Bais
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Group Delay ◽  
Filter Design ◽  
Design Stage ◽  
Artificial Neural
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