[3] [3] IEEE Standard 299, Method for measuring the shielding effectiveness of enclosures and boxes having all dimensions between 0.1 m and 2 m［S］.

[10] [10] Medico R, Lambrecht N, Pues H, et al. Machine learning based error detection in transient susceptibility tests［J］. IEEE Trans Electromagnetic Compatibility, 2019, 61(2): 352-360.

[11] [11] Trinchero R, Manfredi P, Stievano I S, et al. Machine learning for the performance assessment of high-speed links［J］. IEEE Trans Electromagnetic Compatibility, 2018, 60(6): 1627-1634.

[12] [12] Br′eard A, Moulla R, Vollaire C. Metamodel of power electronic converters using learning SVR method coupling with wavelet compression［J］. IEEE Trans Electromagnetic Compatibility, 2016, 58(2): 588-598.

[13] [13] Lozano A, Robinson M P, Diaz A, et al. Evaluation and optimization of an equivalent model for printed circuit boards inside metallic enclosures［C］//XXIX General Assembly Int. Union Radio Science. 2008: EBp6.

[14] [14] Marvin A C, Dawson J F, Ward S, et al. A proposed new definition and measurement of the shielding effect of equipment enclosures［J］. IEEE Trans Electromagnetic Compatibility, 2004, 46(3): 459-468.

[16] [16] Breiman L. Random forests［J］. Machine Learning, 2001, 45(1): 5-32.

[17] [17] Horvath L, Kokoszka P. A bootstrap approximation to a unit root test statistic for heavy-tailed observations［J］. Statistics and Probability Letters, 2003, 63(2): 163-173.