## 3 The Objective Electromagnetic Environmental Effects Assessment
### Adaptive Evaluation Method  
With the increasing complexity of the electromagnetic environment, more and more evaluation indexes are needed to improve the accuracy of evaluation. Accordingly, the calculation and update of each index weighting will become a crucial issue for future research. Not only will the subjective outcome be obtained by the traditional empirical weighting method, but also complicated calculations will be obtained by the mathematical method. However, adaptive evaluation models based on neural networks can effectively solve the above problems. By constructing neural network models, weightings can be self-trained based on existing data features. Subsequently, an objective evaluation result of electromagnetic environmental complexity can be obtained. Figure 5 illustrates the general evaluation process of electromagnetic environmental effects based on neural networks. [PERSON] et al. [42] put forward a kind of electromagnetic environmental complexity evaluation method based on feed-forward back-propagation (BP) neural network algorithms adopting the spectrum occupancy (SO) rate, time occupancy (TO) rate, airspace coverage (AC), and background noise intensity (BN). To realize the learning and training of weightings, it also solves the subjectivity and complicated calculation problems of the traditional methods. [PERSON] et al. [43] believed that various signals show different impacts on the complexity of the electromagnetic environment. Hence, the conventional statistical characteristics of the time, space, frequency, and energy domain indexes may not be able to represent the complexity of the electromagnetic environment. However, a few abnormal activities in significant areas may account for a high proportion of the calculation of the electromagnetic environmental complexity. Based on this, the adaptive fuzzy neural network model is constructed to evaluate the complexity of the electromagnetic environment by using seven indexes: abnormal signal rate (AS), large signal rate (LS), time occupancy (TO), space coverage (SC), channel occupancy (CO), average power spectral density (AP), and background noise intensity (BN). [PERSON] et al. [44] feed the evaluation indexes of time domain occupancy (TO), frequency domain occupancy (FO), energy occupancy (EO), frequency overlap (FP), signal modulation format (SM), channel occupancy (CO), and background noise intensity (BN) of electromagnetic signals to the extreme learning machine integration model to evaluate the electromagnetic environment's complexity. This method also belongs to the category of BP neural networks. Typical research with adaptive methods is shown in Table 4. The adaptive intelligent method uses the testing data of the electromagnetic environment in four domains, extracts  
Figure 5: Electromagnetic environmental effect evaluation process based on a neural network. the characteristics of the data as network input, and trains the evaluation model parameters based on a neural network. The key is to determine the number of hidden layers and the number of hidden layer neurons. Overcomplicated hidden layer structures may lead to overfitting of the model, while oversimplified hidden layers may lead to underfitting. Therefore, we should select the appropriate network structure. Single hidden layer networks can use the cross-verification method [42], experimental methods (incremental and pruning methods) [44], and others. The fuzzy interference method [43] can be adopted for complex network structures. Although the neural network method can adaptively adjust the model weightings to obtain relatively objective results, there are still some problems in the current research. First, the limited amount of electromagnetic environment data may weaken the model's performance. Second, the training and verification are carried out in the same electromagnetic environment, so the application ability of the model in other electromagnetic environments is worthy of further discussion.