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

This article proposes a multifeature-assisted neuro-transfer function (neuro-TF) surrogate-based electromagnetic (EM) optimization technique exploiting trust-region algorithms for microwave filter design. The proposed optimization technique addresses the situation where the response of the starting...

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Published in:	IEEE transactions on microwave theory and techniques Vol. 68; no. 2; pp. 531 - 542
Main Authors:	Feng, Feng, Na, Weicong, Liu, Wenyuan, Yan, Shuxia, Zhu, Lin, Ma, Jianguo, Zhang, Qi-Jun
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Computational modeling Design optimization Design specifications Electromagnetic (EM) optimization feature Feature extraction Filter design (mathematics) Linear programming microwave filter Microwave filters neuro-transfer function (neuro-TF) Optimization Optimization techniques Parameter identification Parameter modification Specifications Transfer functions trust region
ISSN:	0018-9480, 1557-9670
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Abstract	This article proposes a multifeature-assisted neuro-transfer function (neuro-TF) surrogate-based electromagnetic (EM) optimization technique exploiting trust-region algorithms for microwave filter design. The proposed optimization technique addresses the situation where the response of the starting point is far away from the design specifications. We propose to utilize multiple feature parameters to help move the passband of the filter response into the range of design specifications. The pole-zero-based neuro-TF is introduced in this article to help extract the multiple feature parameters when the feature parameters of filter responses are not explicitly identified. Furthermore, we propose to derive new optimization objective functions to involve the multiple feature parameters. A new trust-region updating formulation for the modified optimization objective functions is derived to guarantee the optimization convergence. With the assistance of multiple feature parameters, the proposed surrogate-based EM optimization has a better capability of avoiding local minima and can reach the optimal EM solution faster than the surrogate-based EM optimizations without feature assistance. Three examples of EM optimizations of microwave filters are used to demonstrate the proposed technique.
AbstractList	This article proposes a multifeature-assisted neuro-transfer function (neuro-TF) surrogate-based electromagnetic (EM) optimization technique exploiting trust-region algorithms for microwave filter design. The proposed optimization technique addresses the situation where the response of the starting point is far away from the design specifications. We propose to utilize multiple feature parameters to help move the passband of the filter response into the range of design specifications. The pole-zero-based neuro-TF is introduced in this article to help extract the multiple feature parameters when the feature parameters of filter responses are not explicitly identified. Furthermore, we propose to derive new optimization objective functions to involve the multiple feature parameters. A new trust-region updating formulation for the modified optimization objective functions is derived to guarantee the optimization convergence. With the assistance of multiple feature parameters, the proposed surrogate-based EM optimization has a better capability of avoiding local minima and can reach the optimal EM solution faster than the surrogate-based EM optimizations without feature assistance. Three examples of EM optimizations of microwave filters are used to demonstrate the proposed technique.
Author	Na, Weicong Yan, Shuxia Zhu, Lin Feng, Feng Zhang, Qi-Jun Ma, Jianguo Liu, Wenyuan
Author_xml	– sequence: 1 givenname: Feng orcidid: 0000-0002-3569-8782 surname: Feng fullname: Feng, Feng email: fengfeng@doe.carleton.ca organization: Department of Electronics, Carleton University, Ottawa, Canada – sequence: 2 givenname: Weicong orcidid: 0000-0001-9775-5124 surname: Na fullname: Na, Weicong email: weicongna@bjut.edu.cn organization: Faculty of Information Technology, Beijing University of Technology, Beijing, China – sequence: 3 givenname: Wenyuan surname: Liu fullname: Liu, Wenyuan email: liuwenyuan@sust.edu.cn organization: College of Electrical and Information Engineering, Shaanxi University of Science and Technology, Xi'an, China – sequence: 4 givenname: Shuxia orcidid: 0000-0002-1991-3526 surname: Yan fullname: Yan, Shuxia email: tjuysx@163.com organization: School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China – sequence: 5 givenname: Lin surname: Zhu fullname: Zhu, Lin email: zhulin@tcu.edu.cn organization: School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, China – sequence: 6 givenname: Jianguo surname: Ma fullname: Ma, Jianguo email: mjg@gdut.edu.cn organization: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China – sequence: 7 givenname: Qi-Jun orcidid: 0000-0001-7852-5331 surname: Zhang fullname: Zhang, Qi-Jun email: qjz@doe.carleton.ca organization: Department of Electronics, Carleton University, Ottawa, Canada
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SubjectTerms	Algorithms Computational modeling Design optimization Design specifications Electromagnetic (EM) optimization feature Feature extraction Filter design (mathematics) Linear programming microwave filter Microwave filters neuro-transfer function (neuro-TF) Optimization Optimization techniques Parameter identification Parameter modification Specifications Transfer functions trust region
Title	Multifeature-Assisted Neuro-transfer Function Surrogate-Based EM Optimization Exploiting Trust-Region Algorithms for Microwave Filter Design
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