Interpretable Deep Learning for Nonlinear System Identification Using Frequency Response Functions With Ensemble Uncertainty Quantification

Interpretable Deep Learning for Nonlinear System Identification Using Frequency Response Functions With Ensemble Uncertainty Quantification

Deep learning methods contain powerful tools for modelling nonlinear dynamic systems. However, whilst these models are useful for predicting outputs, they tend to be described by complicated black box equations that lack interpretability. They are therefore not so useful for giving insight into syst...

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Bibliographic Details
Main Authors: Will R. Jacobs, Visakan Kadirkamanathan, Sean R. Anderson
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deep learning
nonlinear system identification
frequency response functions
uncertainty quantification
ensemble methods
Online Access:https://ieeexplore.ieee.org/document/10398181/

Internet

https://ieeexplore.ieee.org/document/10398181/

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