Hai-Ying Luo, W. Shao, Xiao Ding, Bing-Zhong Wang, Xi Cheng
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引用次数: 2
Abstract
An effective convolutional neural network (CNN) with the transfer function (TF) is proposed for shape modeling of electromagnetic (EM) behaviors of microstrip filters. The input of CNN is the images of metallic strips instead of the geometric parameters. To define the training samples, a one-to-one relation between the strip contour and the knot positions is built with a shape-changing technique based on cubic spline interpolation. The proposed model is confirmed with an example of a microstrip/coplanar waveguide (CPW) ultrawideband (UWB) filter. Compared with the parametric artificial neural network (ANN) and the shape ANN, the proposed model shows the improvement of design flexibility and the expansion of the solution domain.
期刊介绍:
The IEEE Microwave and Wireless Components Letters (MWCL) publishes four-page papers (3 pages of text + up to 1 page of references) that focus on microwave theory, techniques and applications as they relate to components, devices, circuits, biological effects, and systems involving the generation, modulation, demodulation, control, transmission, and detection of microwave signals. This includes scientific, technical, medical and industrial activities. Microwave theory and techniques relates to electromagnetic waves in the frequency range of a few MHz and a THz; other spectral regions and wave types are included within the scope of the MWCL whenever basic microwave theory and techniques can yield useful results. Generally, this occurs in the theory of wave propagation in structures with dimensions comparable to a wavelength, and in the related techniques for analysis and design.