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Classification for Dynamical Systems: Model-Based and Data-Driven Approaches
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DOI：10.1109/TAC.2020.2998975.png)
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摘要
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We address the problem of classifying trajectories generated by dynamical systems. We consider the model-based approach, which is the classic approach in control theory, and (data-driven) support vector machines, a popular method in the area of machine learning. The analysis points out connections between the two approaches and their relative merits. Examples are given to substantiate the analysis.
Keyword:
Support vector machines
Brain modeling
Trajectory
Data models
Dynamical systems
Training
Linear systems
Classification algorithms
dynamical systems
machine learning
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期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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