Modeling of microwave filters using gradient Particle Swarm Optimization neural networks

Erredir, Ch.; Riabi, Mohamed Lahdi; Ammari, Halima; Bouarroudj, Emir

doi:10.1109/dat.2017.7889160

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…8th order microstrip filter (12 variables). 10th order waveguide diplexer (22 variables); 8th order waveguide diplexer (23 variables) Hybrid algorithm GA algorithm + Nelder-Mead simplex algorithm [106], [107] 4 th order ridged waveguide filter (9 variables) 4 th order dielectric filter (7 variables) Gradient Particle Swarm [108] 4 th order waveguide filter (6 variables)…”

Section: B Sao Techniques For Novel Design Applicationsmentioning

confidence: 99%

State-of-the-Art: AI-Assisted Surrogate Modeling and Optimization for Microwave Filters

Zhang

Cheng

et al. 2022

IEEE Trans. Microwave Theory Techn.

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Microwave filters are indispensable passive devices for modern wireless communication systems. Nowadays, electromagnetic (EM) simulation-based design process is a norm for filter designs. Many EM-based design methodologies for microwave filter design have emerged in recent years to achieve efficiency, automation, and customizability. The majority of EM-based design methods exploit low-cost models (i.e., surrogates) in various forms and artificial intelligence techniques assist the surrogate modeling and optimization processes. Focusing on surrogate assisted microwave filters designs, this paper firstly analyzes the characteristic of filter design based on different design objective functions. Then, the state-of-the-art filter design methodologies are reviewed, including surrogate modeling (machine learning) methods and advanced optimization algorithms. Three essential techniques in filter designs are included: 1) Smart data sampling techniques; 2) Advanced surrogate modeling techniques. 3) Advanced optimization methods and frameworks. To achieve success and stability, they have to be tailored or combined together to achieve the specific characteristics of the microwave filters. Finally, new emerging design applications and future trends in filter design are discussed.

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Section: B Sao Techniques For Novel Design Applicationsmentioning

confidence: 99%

State-of-the-Art: AI-Assisted Surrogate Modeling and Optimization for Microwave Filters

Zhang

Cheng

et al. 2022

IEEE Trans. Microwave Theory Techn.

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show abstract

“…1 Once a neural network is trained well, not only can it be used repeatedly, but also its response speed is beyond the reach of EM simulation. For these reasons, neural networks have been used for various filter modeling and design applications, including dielectric resonator filter, 2 rectangular waveguide Hplane iris bandpass filter, 3,4 microstrip bandpass filters, 5 and so on.…”

Section: Introductionmentioning

confidence: 99%

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

Jin

2020

Int J RF Microw Comput Aided Eng

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In this article, we propose a novel neural network of calibrated coarse model, which can obtain the optimal filter response with as little training data as possible to synthesize the entire substrate integrated waveguide (SIW) filter. By incorporating the knowledge of filter decomposition with the inverse neural network, we build a coarse model that can synthesize the dimensions of a SIW filter. However, the SIW structures are subject to a potential leakage problem due to the periodic gaps, the results of the coarse model are very different from the ideal response. We propose a novel calibrated neural network from the perspective of the coupling matrix to correct the errors generated in the coarse model. In addition, this article also proposes an equivalent de‐embedding technique, which is simpler than the thru‐reflect‐line calibration technique to accurately extract the scattering parameters of the SIW discontinuities. An H‐plane fifth order SIW filter is synthesized by the proposed model. The result shows that the SIW filter that is very close to the ideal response can be synthesized with only a few hundred training data.

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Modeling of microwave filters using gradient Particle Swarm Optimization neural networks

Cited by 2 publications

References 12 publications

State-of-the-Art: AI-Assisted Surrogate Modeling and Optimization for Microwave Filters

State-of-the-Art: AI-Assisted Surrogate Modeling and Optimization for Microwave Filters

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

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