An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

中文翻译：

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航空公司竞争力的智能评估方法

提出了一种智能评估方法来分析航空公司的竞争力。从安全，服务和规范的角度，我们建立了交通权的竞争力指标和标准样本库。自组织映射（SOM）神经网络用于在没有监督和先验知识的情况下对样本进行自组织和自学习。基于多步设置确定高收敛速度和高聚类精度的训练步骤。利用典型的航空公司指数数据来验证自组织映射神经网络对航空公司竞争力分析的影响。仿真结果表明，自组织映射神经网络可以准确有效地对航空公司的竞争力进行分类和评价，