Planar inverted F-shaped antenna resonant frequency prediction method based on semi-supervised learning

Abstract

The invention discloses a planar inverted F-shaped antenna resonant frequency prediction method based on semi-supervised learning. The method comprises the following steps: establishing a mapping relationship between four related parameters of the width of a short-circuit metal sheet, the length of a radiation metal sheet, the width of the radiation metal sheet and the height of the radiation metal sheet of the planar inverted F-shaped antenna and an actually measured resonant frequency by using a Gaussian process and a support vector machine; carrying out iterative training by utilizing a cooperative training method of a Gaussian process and a support vector machine in combination with unmarked data, wherein the trained semi-supervised cooperative training model can be used for predictingresonant frequencies of other planar inverted F-shaped antennas. According to the method, the problems that in existing electromagnetic optimization design, more marking samples are needed during model training, electromagnetic simulation software HFSS needs to be called for multiple times, the calculation cost is high, and consumed time is long can be solved; compared with a modeling mode basedon traditional supervised learning, the resonant frequency prediction capability of the method has certain advantages.

Description

technical field [0001] The invention relates to a method for predicting the resonant frequency of a planar inverted-F antenna based on semi-supervised learning, which belongs to the field of electromagnetic optimization design. Background technique [0002] In the field of optimal design of electromagnetic devices, numerical simulation calculations or electromagnetic simulation software such as HFSS (High Frequency Structure Simulator) combined with optimization algorithms are commonly used. Highly accurate results can be obtained through HFSS software simulation to obtain labeled training data for training. When HFSS is called through the optimization algorithm, if the microwave device has a complex structure, large size, and multiple frequency bands, it needs to be called multiple times, and each time HFSS is called to evaluate the individual, it takes a lot of time, and the calculation cost is high and time-consuming. Therefore, using a modeling method instead of calling...

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Problems solved by technology

[0005] Purpose of the invention: In order to overcome the problems of the existing electromagnetic optimization design, the need for more labeled samples when training the model, the need to call the electromagnetic simulation software HFSS multiple times, the high cost of calculation and the lon

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