How can the stability and efficiency of Artificial Neural Networks (ANNs) be ensured through a systematic analysis method? This paper seeks to address that query. While numerous factors can influence the learning process of ANNs, utilizing knowledge from control systems allows us to analyze its system function and simulate system responses. Although the complexity of most ANNs is extremely high, we still can analyze each factor (e.g., optimiser, hyperparameters) by simulating their system response. This new method also can potentially benefit the development of new optimiser and learning system, especially when discerning which components adversely affect ANNs. Controlling ANNs can benefit from the design of optimiser and learning system, as (1) all optimisers act as controllers, (2) all learning systems operate as control systems with inputs and outputs, and (3) the optimiser should match the learning system. Please find codes: \url{https://github.com/RandomUserName2023/Control-ANNs}.

翻译：如何通过系统分析方法确保人工神经网络（ANNs）的稳定性和效率？本文旨在解决这一问题。尽管众多因素会影响人工神经网络的学习过程，但利用控制系统的知识，我们可以分析其系统函数并模拟系统响应。尽管大多数人工神经网络的复杂程度极高，我们仍可通过模拟各因素（如优化器、超参数）的系统响应对其进行分析。这一新方法还可能有助于新型优化器及学习系统的开发，尤其在识别对人工神经网络产生不利影响的组件方面。对人工神经网络的控制可受益于优化器与学习系统的设计，其核心在于：（1）所有优化器均充当控制器；（2）所有学习系统均作为具有输入与输出的控制系统运行；（3）优化器应与学习系统相匹配。相关代码请见：\url{https://github.com/RandomUserName2023/Control-ANNs}。