The performance of a single neural network can vary unexpectedly corresponding to different classification tasks, and thus the network with fixed structure may lack flexibility and often lead to overfitting or underfitting. It is urgent, also the main objective of this paper, to deal with the limitation of the fixed neural network structure on classifying radar signals in different electromagnetic environments. We in this paper propose a variable network architecture search (NAS) mechanism, called balanced-NAS framework , and apply it in specific emitter identification (SEI) to greatly improve the flexibility of model searching. In the proposed balanced-NAS framework, a “block-cell” structure and a controller based recurrent neural network (RNN) are utilized to design models automatically according to external environment. In particular, a balance function is also proposed and utilized in the balanced-NAS framework, acting on the RNN controller to take both the validation accuracy and computational budget into consideration while searching for ideal models. The efficiency of the searching process is further enhanced by exploiting a progressive strategy to design simple and complicate child models where unpromising ones after each evaluation process are obsoleted to release searching space. Simulations and experiments indicate that the proposed balanced-NAS framework is extremely efficient and outperforms the conventional algorithms in classifying radar signals in different environments.

中文翻译：

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平衡神经架构搜索及其在特定发射器识别中的应用

单个神经网络的性能可能会因不同的分类任务而出乎意料地变化，因此结构固定的网络可能缺乏灵活性，经常导致过拟合或欠拟合。解决固定神经网络结构对不同电磁环境下雷达信号分类的局限性，也是本文的主要目标。我们在本文中提出了一种可变网络架构搜索（NAS）机制，称为balance-NAS 框架，并将其应用于特定发射器识别（SEI），大大提高了模型搜索的灵活性。在提出的平衡 NAS 框架中，利用“块单元”结构和基于控制器的循环神经网络 (RNN) 根据外部环境自动设计模型。特别是，在平衡 NAS 框架中还提出并使用了平衡函数，作用于 RNN 控制器，在搜索理想模型时同时考虑验证精度和计算预算。通过利用渐进式策略来设计简单和复杂的子模型，其中在每个评估过程后废弃没有希望的模型以释放搜索空间，从而进一步提高了搜索过程的效率。