Neural Network Method for Dual-Mode Dual-Band DSPSL Bandpass Filter Design

Author(s):  
Shan Wang ◽  
Yao Wang ◽  
Yang Jiang ◽  
Haiwen Liu
2021 ◽  
Vol 57 (8) ◽  
pp. 328-330
Author(s):  
Chad Bartlett ◽  
Michael Höft

2012 ◽  
Vol 54 (10) ◽  
pp. 2370-2372 ◽  
Author(s):  
Haiwen Liu ◽  
Yulong Zhao ◽  
Liyun Shi ◽  
Hui Luo

2014 ◽  
Vol 7 (2) ◽  
pp. 127-133 ◽  
Author(s):  
Mushtaq Alqaisy ◽  
Chandan Chakrabraty ◽  
Jawad Ali ◽  
Adam R.H. Alhawari

In this paper, a fractal-based complementary split-ring resonator (CSRR) has been introduced as a defected ground structure (DGS) in the ground plane of a dual-mode microstrip bandpass filter to produce a new compact filter with dual-band response. The conventional double square ring resonator structure is modified such that its inner ring is made with a fractal shape instead of a square. Measured and simulation results show that the resulting filter offers a dual passband response; the higher passband is attributed to the dual-mode microstrip ring structure, whereas the lower passband is as a result of the embedded CSRR DGS structure. In addition, the results show that the position of the lower passband could be varied, to a certain extent, without affecting the position of the higher passband by applying higher fractal iteration levels to the inner split ring. These features, together with the compact size the proposed filter offers, make it suitable for use in a wide variety of dual-band communication applications. Measured results, carried out on filter prototypes, have been found in agreement with those theoretically predicted.


Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.


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