Many current industry branches use hybrid approaches to solve complex application problems. Over the last decades, different tools for the simulation of such hybrid systems (e.g. Hysdel and YAMLIP) as well as the identification of hybrid systems (e.g. HIT, MLP and OAF NN) have been developed. The framework presented in this work facilitates the integration of artificial feed-forward neural networks in the modelling process of hybrid dynamical systems (HDS). Additionally, the framework provides a structured language for characterising these feed-forward networks itself. Therefore, an interdisciplinary exchange in the field of neural networks and its integration into hybrid dynamical systems is enabled. Focusing on hybrid systems with autonomous events, two different approaches, namely the artificial hybrid model and the artificial hybrid dynamics, are introduced. Challenges of the modelling process of HDS are reflected and advantages as well as disadvantages are discussed. The case study includes two common examples of HDS and analyses the simulation results and examines limitations of the modelling framework.

许多当前的行业分支使用混合方法来解决复杂的应用程序问题。在过去的几十年中，已经开发了用于模拟此类混合系统（例如 Hysdel 和 YAMLIP）以及识别混合系统（例如 HIT、MLP 和 OAF NN）的不同工具。这项工作中提出的框架促进了人工前馈神经网络在混合动力系统 (HDS) 的建模过程中的集成。此外，该框架提供了一种结构化语言来表征这些前馈网络本身。因此，神经网络领域的跨学科交流及其与混合动力系统的集成得以实现。专注于具有自主事件的混合系统，两种不同的方法，即人工混合模型和人工混合动力学，介绍。反映了 HDS 建模过程的挑战，并讨论了优缺点。案例研究包括 HDS 的两个常见示例，并分析了仿真结果并检查了建模框架的局限性。