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This project deals with learning to reproduce the input-output behavior of state-space models using recurrent neural networks and the Keras machine learning toolbox.

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AleksandarHaber/Machine-Learning-of-Dynamical-Systems-using-Recurrent-Neural-Networks

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Machine Learning of Dynamical Systems Using Recurrent Neural Networks

This project deals with learning to reproduce the input-output behavior of state-space models using recurrent neural networks and the Keras machine learning toolbox.

  • The file "system_identification_machine_learning.py" is the main file. You should start from here.
  • The file "backward euler.py" defines a function for discretizing the continuous-time system using the backward Euler method. It is called from the file "system_identification_machine_learning.py". The complete description of this project is given on my webpage: https://aleksandarhaber.github.io/

CODE COPYRIGHT NOTICE AND LICENSE: THE CODE FILES POSTED ON THIS WEBSITE ARE NOT FREE SOFTWARE AND CODE. IF YOU WANT TO USE THIS CODE IN THE COMMERCIAL SETTING OR ACADEMIC SETTING, THAT IS, IF YOU WORK FOR A COMPANY OR IF YOU ARE AN INDEPENDENT CONSULTANT AND IF YOU WANT TO USE THIS CODE OR IF YOU ARE ACADEMIC RESEARCHER OR STUDENT, THEN WITHOUT MY PERMISSION AND WITHOUT PAYING THE PROPER FEE, YOU ARE NOT ALLOWED TO USE THIS CODE. YOU CAN CONTACT ME AT [email protected]
TO INFORM YOURSELF ABOUT THE LICENSE OPTIONS AND FEES FOR USING THIS CODE. ALSO, IT IS NOT ALLOWED TO (1) MODIFY THIS CODE IN ANY WAY WITHOUT MY PERMISSION. (2) INTEGRATE THIS CODE IN OTHER PROJECTS WITHOUT MY PERMISSION. (3) POST THIS CODE ON ANY PRIVATE OR PUBLIC WEBSITES OR CODE REPOSITORIES. DELIBERATE OR INDELIBERATE VIOLATIONS OF THIS LICENSE WILL INDUCE LEGAL ACTIONS AND LAWSUITS.

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This project deals with learning to reproduce the input-output behavior of state-space models using recurrent neural networks and the Keras machine learning toolbox.

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