Summary of Deep Learning Of Dynamic Systems Using System Identification Toolbox(tm), by Tianyu Dai et al.
Deep Learning of Dynamic Systems using System Identification Toolbox(TM)
by Tianyu Dai, Khaled Aljanaideh, Rong Chen, Rajiv Singh, Alec Stothert, Lennart Ljung
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent release in the System Identification Toolbox from MATLAB has seen significant advancements in dynamic modeling capabilities. The toolbox now seamlessly integrates deep learning architectures and training techniques to utilize deep neural networks as building blocks for nonlinear models. Specifically, it offers neural state-space models that can be augmented with auto-encoding features, ideal for reduced-order modeling of large systems. Additionally, the toolbox has been enhanced to leverage auto-differentiation features for state estimation, enabling direct use of raw numeric matrices and timetables for training models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A recent update in MATLAB’s System Identification Toolbox has made it easier to build complex models. It now combines deep learning techniques with traditional modeling methods. This means you can create more accurate models that take into account non-linear relationships between variables. The toolbox also includes new features for reducing the complexity of large systems, making it easier to understand and predict how they behave. |
Keywords
» Artificial intelligence » Deep learning
