Differential Evolution Based Manifold Gaussian Process Machine Learning for Microwave Filter’s Parameter Extraction

Gaussian process (GP) is a rapidly developing supervised machine learning (ML) method in recent years, which has been widely used in the establishment of surrogate models in the field of electromagnetics. However, it has the problems of large sample demand, high computational complexity and low accuracy when processing high dimensional data. To solve this problem, a manifold Gaussian process (MGP) ML method based on differential evolution (DE) algorithm is proposed in this study. For the proposed method, the DE algorithm is used to get dimension reduction parameters, and the method can work very well with the optimized parameters. Compared with the traditional GP model, the dimensionality reduction method based on Isomap is adopted to simplify the mapping relationship between data pairs. Therefore, the model is more suitable for the problem of insufficient samples and high data dimension. In this study, the proposed DE-based MGP (DE-MGP) is applied to the extraction of coupling coefficients of the fourth-order and sixth-order coupling filters, in which the test error of the fourth-order coupling filter surrogate model can be reduced to 0.84%, and the test error of the sixth-order coupling filter is expected to be reduced to 1.53%, which proves that the proposed method is very effective.

[1]  Jing Gao,et al.  Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning , 2020, Complex..

[2]  Wei Shao,et al.  Dynamic Adjustment Kernel Extreme Learning Machine for Microwave Component Design , 2018, IEEE Transactions on Microwave Theory and Techniques.

[3]  Yuji Maeda,et al.  High-speed, automatic and optimal design of microwave filter using genetic algorithms , 2014, 2014 International Symposium on Antennas and Propagation Conference Proceedings.

[4]  J. P. Jacobs,et al.  Gaussian-Process-Regression-Based Design of Ultrawide-Band and Dual-Band CPW-FED Slot Antennas , 2010 .

[5]  J. P. Jacobs,et al.  Single-model versus ensemble-model strategies for efficient Gaussian process surrogate modeling of antenna input characteristics , 2013, 2013 International Conference on Electromagnetics in Advanced Applications (ICEAA).

[6]  Robert B. Gramacy,et al.  Ja n 20 08 Bayesian Treed Gaussian Process Models with an Application to Computer Modeling , 2009 .

[7]  Lan Xu,et al.  Optimisation of reflection coefficient of microstrip antennas based on KBNN exploiting GPR model , 2018 .

[8]  J. P. Jacobs,et al.  GAUSSIAN PROCESS MODELING OF CPW-FED SLOT ANTENNAS , 2009 .

[9]  Slawomir Koziel,et al.  Reduced‐cost microwave filter modeling using a two‐stage Gaussian process regression approach , 2015 .

[10]  S. Cohn.  Direct-Coupled-Resonator Filters , 1957, Proceedings of the IRE.

[11]  Dong Yue,et al.  Frequency characteristics of electromagnetic bandgap structure with bow‐tie cells and its optimal design based on particle swarm optimization , 2013 .

[12]  Bo Liu,et al.  Global Optimization of Microwave Filters Based on a Surrogate Model-Assisted Evolutionary Algorithm , 2017, IEEE Transactions on Microwave Theory and Techniques.

[13]  Chao Zhang,et al.  Parametric Modeling of EM Behavior of Microwave Components Using Combined Neural Networks and Pole-Residue-Based Transfer Functions , 2016, IEEE Transactions on Microwave Theory and Techniques.

[14]  Jose Luis Chavez-Hurtado,et al.  Polynomial-Based Surrogate Modeling of RF and Microwave Circuits in Frequency Domain Exploiting the Multinomial Theorem , 2016, IEEE Transactions on Microwave Theory and Techniques.

[15]  J. P. Jacobs,et al.  Efficient Resonant Frequency Modeling for Dual-Band Microstrip Antennas by Gaussian Process Regression , 2015, IEEE Antennas and Wireless Propagation Letters.

[16]  J. Rhodes.  The Theory of Generalized Interdigital Networks , 1969 .

[17]  W. P. Mason,et al.  The use of coaxial and balanced transmission lines in filters and wide-band transformers for high radio frequencies , 1937 .

[18]  Mauricio A. Álvarez,et al.  Multi-output Gaussian processes for enhancing resolution of diffusion tensor fields , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[19]  R. Cameron.  General coupling matrix synthesis methods for Chebyshev filtering functions , 1999 .

[20]  Hongqiang Guo,et al.  Power Management for Plug-in Hybrid Electric Vehicle with Automated Mechanical Transmission using Multiple Dimensional Scaling Method , 2020, Recent Patents on Engineering.

[21]  Yong-xiu Xu,et al.  Optimal Design of Conical Horn Antenna Based on GP Model with Coarse Mesh , 2019, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.

[22]  Liping Fu,et al.  Inverse Modeling for Filters Using a Regularized Deep Neural Network Approach , 2020, IEEE Microwave and Wireless Components Letters.

[23]  Vicente E. Boria,et al.  On Space Mapping Techniques for Microwave Filter Tuning , 2019, IEEE Transactions on Microwave Theory and Techniques.

[24]  Tao Shen,et al.  Full-wave design of canonical waveguide filters by optimization , 2001, 2001 IEEE MTT-S International Microwave Sympsoium Digest (Cat. No.01CH37157).

[25]  Weicong Na,et al.  Deep Neural Network Technique for High-Dimensional Microwave Modeling and Applications to Parameter Extraction of Microwave Filters , 2019, IEEE Transactions on Microwave Theory and Techniques.

[26]  S. Koziel,et al.  Two-Stage Framework for Efficient Gaussian Process Modeling of Antenna Input Characteristics , 2014, IEEE Transactions on Antennas and Propagation.

[27]  R. J. Cameron,et al.  Genenal Extracted Pole Synthesis Technique with Applications to Low-Loss TE/sub 011/ Mode Filters , 1980 .

[28]  Rui Yang,et al.  Path Optimization Study for Vehicles Evacuation based on Dijkstra Algorithm , 2014 .

[29]  S. Amari,et al.  Synthesis of cross-coupled resonator filters using an analytical gradient-based optimization technique , 2000 .

[30]  A. Atia,et al.  Narrow-Bandpass Waveguide Filters , 1972 .

[31]  Heng-Tung Hsu,et al.  Parameter extraction for symmetric coupled-resonator filters , 2002, 2002 IEEE MTT-S International Microwave Symposium Digest (Cat. No.02CH37278).

[32]  M. Cacciola,et al.  Microwave Devices and Antennas Modelling by Support Vector Regression Machines , 2006, IEEE Transactions on Magnetics.