Identification of Nonlinear Local Model State Space Networks

本计画将探讨非线性系统辨识之新方法。它融合了机器学习和系统识别社区的思想。本项目的目标是开发一种新型的模型结构和相关的训练算法，用于构建数据驱动的非线性状态空间模型。它们基于局部线性模型网络，与现有方法相比将提供许多好处。这些方法有助于工业中对值得信赖的人工智能的需求。 将开发一种用于从数据构建局部模型状态空间网络（LMSSNs）的训练算法。因此，重点将是增量树构造策略的状态空间建模的转移。各种挑战来自状态向量的递归，并在本项目中得到解决。为了以有效和高效的方式进行LMSSN的训练，研究了受机器学习方法启发的新的优化方案。一个合适的局部模型的参数化需要通过考虑规范形式，面向块的结构，和/或正则化技术。因此，特别注意的鲁棒性和外推行为的关键问题。最后，开发的方法进行比较，国家的最先进的替代品在各种基准问题。此外，他们的实用性进行了测试，对一些现实世界的进程。

This project shall investigate a new approach for nonlinear system identification. It fuses ideas from the machine learning and system identification communities. The goal of this project is to develop a novel class of model structures and associated training algorithms for building data-driven nonlinear state space models. They are based on local linear model networks and will offer many benefits compared to existing approaches. The methods contribute to the demand for trustworthy artificial intelligence in industry. A training algorithm for constructing local model state space networks (LMSSNs) from data will be developed. Thereby, the focus will be on the transfer of incremental tree-construction strategies to state space modeling. Various challenges arise from the recurrency of the state vector and are addressed in this project. In order to carry out the training of LMSSNs in an effective and efficient way, new optimization schemes inspired by machine learning approaches are investigated. A suitable parameterization of the local models needs to be found by considering canonical forms, block-oriented structures, and/or regularization techniques. Hereby special care is given to the crucial issues of robustness and extrapolation behavior. Finally, the developed methods are compared to state-of-the-art alternatives on various benchmark problems. Furthermore, their practicability is tested on a number of real-world processes.