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In this chapter, we introduce the Algorithmic Deep Learning Neural Network (ADLNN), a deep learning system that incorporates algorithmic descriptions of the processes as part of the deep learning neural network. The dynamical models provide domain knowledge. These are in the form of differential equations. The outputs of the network are both indications of failures and updates to the parameters of the models. Training can be done using simulations, prior to operations, or through operator interaction during operations.
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PhD thesis of Jere Schenck Meserole, “Detection Filters for Fault-Tolerant Control of Turbofan Engines,” Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1981.
Stephanie Thomas and Michael Paluszek. MATLAB Machine Learning. Apress, 2017.
Stephanie Thomas and Michael Paluszek. MATLAB Machine Learning Recipes: A Problem-Solution Approach. Apress, 2019. MATH
- Algorithmic Deep Learning
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- Chapter 5