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In power electronic devices, high-speed switching will often lead to serious electromagnetic interference (EMI) problems, which seriously affects the reliability of power electronic systems. To solve these EMI problems, EMI filters are a common solution. The insertion loss is an evaluation index for the noise attenuation capability, and the accuracy of its model directly affects the parameter design accuracy of EMI filters. To improve the prediction accuracy of the EMI filter insertion loss model, accurately describe the system behavior and predict the filtering performance of the EMI filter, and improve the design efficiency of the EMI filter, the insertion loss of a single-stage differential-mode EMI filter is modeled using a back propagation neural network. The proposed neural network model has better practical application value than the ideal model and the behavioral model of a high-frequency circuit, aiming to provide guidance for the design and optimization of EMI filters. This model can quickly evaluate the actual insertion loss of EMI filters to improve their design efficiency.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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