Multifeature-Assisted Neuro-transfer Function Surrogate-Based em Optimization Exploiting Trust-Region Algorithms for Microwave Filter Design
IEEE Transactions on Microwave Theory and Techniques , Volume 68 - Issue 2 p. 531- 542
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.
|Electromagnetic (EM) optimization, feature, microwave filter, neuro-transfer function (neuro-TF), trust region|
|IEEE Transactions on Microwave Theory and Techniques|
|Organisation||Department of Electronics|
Feng, F. (Feng), Na, W. (Weicong), Liu, W. (Wenyuan), Yan, S. (Shuxia), Zhu, L. (Lin), Ma, J. (Jianguo), & Zhang, Q.J. (2020). Multifeature-Assisted Neuro-transfer Function Surrogate-Based em Optimization Exploiting Trust-Region Algorithms for Microwave Filter Design. IEEE Transactions on Microwave Theory and Techniques, 68(2), 531–542. doi:10.1109/TMTT.2019.2952101