An Unsupervised Microwave Filter Design Optimization Method Based on a Hybrid Surrogate Model-Assisted Evolutionary Algorithm-Reference-Cited by-同舟云学术

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An Unsupervised Microwave Filter Design Optimization Method Based on a Hybrid Surrogate Model-Assisted Evolutionary Algorithm

Published:2023-03 Issue:3 Volume:71 Page:1159-1170
ISSN:0018-9480
Container-title:IEEE Transactions on Microwave Theory and Techniques
language:
Short-container-title:IEEE Trans. Microwave Theory Techn.

Author:

Xue Liyuan¹^ORCID,Liu Bo¹^ORCID,Yu Yang²^ORCID,Cheng Qingsha S.³^ORCID,Imran Muhammad¹^ORCID,Qiao Tianrui³

Affiliation:

1. James Watt School of Engineering, University of Glasgow, Glasgow, U.K

2. Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences, Beijing, China

3. Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China

Funder

University of Glasgow Industrial Doctoral Project in collaboration with Nokia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiation

Link

http://xplorestaging.ieee.org/ielx7/22/10061518/09960817.pdf?arnumber=9960817

Reference32 articles.

1. Multifeature-Assisted Neuro-transfer Function Surrogate-Based EM Optimization Exploiting Trust-Region Algorithms for Microwave Filter Design

2. Cognition-Driven Formulation of Space Mapping for Equal-Ripple Optimization of Microwave Filters

3. Microwave Filters for Communication Systems

4. Homotopy Optimization of Microwave and Millimeter-Wave Filters Based on Neural Network Model

5. Design of multiple-passband filters using coupling matrix optimisation

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4. A Novel EM Parametric Modeling Method of Microwave Filters Incorporating Multivalued Neural Networks and Transfer Functions;IEEE Transactions on Microwave Theory and Techniques;2024

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