​Hybrid System Identification helps readers to build mathematical models of dynamical systems switching between different operating modes, from their experimental observations. It provides an overview of the interaction between system identification, machine learning and pattern recognition fields in explaining and analysing hybrid system identification. It emphasises the optimization and computational complexity issues that lie at the core of the problems considered and sets them aside from standard system identification problems. The book presents practical methods that leverage this complexity, as well as a broad view of state-of-the-art machine learning methods.

​混合系统识别有助于读者根据他们的实验观察建立在不同操作模式之间切换的动力系统的数学模型。它概述了在解释和分析混合系统辨识时系统辨识、机器学习和模式识别领域之间的相互作用。它强调了优化和计算复杂性问题，这些问题是所考虑问题的核心，并将其与标准系统识别问题分开。这本书介绍了利用这种复杂性的实用方法，以及最先进的机器学习方法的广泛观点。