## 4 Effect Evaluation of Electromagnetic Environment on Equipment
### Commonly Used Methods  
In the subjective equipment electromagnetic environmental effects assessment, the general objective assessment methods are adopted too. However, the survival probability [49] of the test equipment in the electromagnetic environment should also be considered. [PERSON] et al. [50] proposed a quantitative assessment method for the electromagnetic environmental effects of airborne avionics systems. The electromagnetic sensitivity test data of the equipment is taken as the evaluation reference. The multi-layer fuzzy comprehensive evaluation theory is used as the evaluation method, and the D-S (Dempster-Shafer) evidence theory is employed to process the interference value between the safety margin curve and the sensitivity threshold in the electromagnetic sensitivity test. The critical categories of subsystems and equipment in the national military standard GJB72A-2002 are used to determine the weights. It provides a new way to solve the problem of the electromagnetic environmental complexity evaluation of avionics systems. [PERSON] [51] used the index calculation method based on a fuzzy set and the AHP to study the electromagnetic radiation effects on a vehicle's electronic control system in an electromagnetic pulse environment. [PERSON] et al. [52] believed that useful signals and critical sensitivity played a vital role in the electromagnetic environmental effects evaluation of the equipment. They built an electromagnetic damage effect evaluation model for a ship's electronic equipment by combining the AHP with the comprehensive fuzzy evaluation method. The system's electromagnetic damage degree was divided into five levels to show the equipment performance of the ship's electrical system. In theory, the comprehensive evaluation performance of this model is better. Although it lacks experimental verification and cannot obtain the actual evaluation effect of the model, the application of useful signal and critical sensitivity in this model has great significance for the evaluation of electromagnetic environmental effects. [PERSON] [53] proposed an algorithm combining fuzzy clusters to find the optimal interference waveform that could cause damage to radio fuses. In the mentioned models, multiple indexes [54; 55] are closely related to the test equipment involved. The accurate weighting coefficients of these indexes are always difficult to calculate due to subjective judgment [56], and more performance indicators of the test equipment usually need to be obtained. Not only is the calculation complicated, but also their application is limited. When referring to the application of artificial neural networks in electromagnetic radiation effect predictions, [PERSON] et al. [57] took the electromagnetic interference parameters as the input of the neural network. They established the prediction model with the error backpropagation neural network, which effectively solves the problem of the crosstalk effect between the wires. [PERSON] et al. [58] proposed a method based on artificial neural networks to predict the electromagnetic interference effect of high-voltage transmission lines and public gas transmission pipelines. Subsequently, they took the natural gas pipeline in Oman and Sudan as the test object to verify the effectiveness of this method. Based on the above methods, the researchers explored fusion evaluation methods such as the grey analytic hierarchy process combined with BP neural networks [59], fuzzy theory combined with neural networks [60], and others. The commonly used evaluation methods for equipment are shown in Table 5. According to the literature review, we can divide them into three categories: AHP and fuzzy theory-based methods, neural network-based methods, and fusion methods. The AHP- and fuzzy theory-based method is adopted for effect evaluation on equipment. When a neural network is involved, the model can be used for prediction.