  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}.

Artificial Intelligence