1999
DOI: 10.1002/(sici)1099-047x(199905)9:3<287::aid-mmce12>3.0.co;2-#
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Direct‐coupled cavity filters design using a hybrid feedforward neural network–finite elements procedure

Abstract: In many filtering applications, direct‐coupled cavity filters are often used for their symmetry and handling easiness. In this paper, a method is described for the design of these filtering devices by using an artificial neural network (ANN). Starting from design requirements, the procedure discussed herein directly determines the geometric dimensions of the filtering device with good accuracy and very short processing time. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 287–296, 1999.

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Cited by 19 publications
(6 citation statements)
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“…Waveguide cavity filters are very popular in microwave applications. Several results have been reported using NN techniques to model cavity filters including E‐plane metal‐insert filter [27], rectangular waveguide H‐plane iris bandpass filter [28–31], cylindrical posts in waveguide filter [32], and combline filter [33]. The simplest form of modeling is the direct forward approach where the geometrical parameters are related to its frequency response.…”
Section: Nn‐based Em‐cadmentioning
confidence: 99%
See 1 more Smart Citation
“…Waveguide cavity filters are very popular in microwave applications. Several results have been reported using NN techniques to model cavity filters including E‐plane metal‐insert filter [27], rectangular waveguide H‐plane iris bandpass filter [28–31], cylindrical posts in waveguide filter [32], and combline filter [33]. The simplest form of modeling is the direct forward approach where the geometrical parameters are related to its frequency response.…”
Section: Nn‐based Em‐cadmentioning
confidence: 99%
“…Results show that NNs can provide accurate design parameters and after the learning phase, the computational cost is lower than the one associated with a full wave model analysis [27]. In a similar work, the performance of the filter obtained from the NN was much better than that obtained from parametric curve and faster than that obtained from FEM analysis [28].…”
Section: Nn‐based Em‐cadmentioning
confidence: 99%
“…In recent years, artificial neural networks (ANN) have been widely applied to speed up or to optimize the design process. As a common approach, ANN may take the device geometries as inputs and gives the S parameters as outputs, mimicking the electromagnetic simulation process [9,10]. Once the neural networks are developed, the computation time becomes much faster than the electromagnetic simulator, therefore, it is particularly suitable for designing and optimizing microwave component designs.…”
Section: Introductionmentioning
confidence: 99%
“…cost is lower than the one associated with full wave model analysis [107]. In a similar work the performance of filter obtained from the ANN was much better than obtained from parametric curve and faster than finite element method (FEM) analysis [108].…”
Section: Neural Network Modeling For Microwave Filtermentioning
confidence: 73%
“…Though the neural network inverse model can provide the solution faster than the optimization method, it often encounters the problem of non-uniqueness in the input- [108], low pass microstrip step filter [5], E-plane metal-insert filter [107], coupled microstrip line band pass filter [10], etc. Waveguide dual-mode pseudoelliptic filters are often used in satellite applications due to its high Q, compact size, and sharp selectivity [112].…”
Section: Introductionmentioning
confidence: 99%