https://github.com/forgi86/pytorch-ident
System identification in PyTorch
https://github.com/forgi86/pytorch-ident
deep-learning pytorch system-identification
Last synced: 2 months ago
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System identification in PyTorch
- Host: GitHub
- URL: https://github.com/forgi86/pytorch-ident
- Owner: forgi86
- License: mit
- Created: 2020-05-30T13:46:11.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-31T14:24:33.000Z (about 3 years ago)
- Last Synced: 2025-12-15T21:20:38.087Z (6 months ago)
- Topics: deep-learning, pytorch, system-identification
- Language: Python
- Homepage: https://pytorch-ident.readthedocs.io/en/latest/
- Size: 3.95 MB
- Stars: 27
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# System identification tools in PyTorch
A collection of system identification tools implemented in PyTorch.
* State-space identification methods (see [1], [2], [3], [6])
* Differentiable transfer functions (see [4], [5])
## Examples and Documentation
* Examples are provided in the [**examples**](examples) folder of this repo.
* The API documentation is available at https://pytorch-ident.readthedocs.io/en/latest.
## Installation:
### Requirements:
A Python 3.9 conda environment with
* numpy
* scipy
* matplotlib
* pandas
* pytorch
### Stable version from PyPI
Run the command
```
pip install pytorch-ident
```
This will install the current [stable version](https://pypi.org/project/pytorch-ident/) from the PyPI package repository.
### Latest version from GitHub
1. Get a local copy the project. For instance, run
```
git clone https://github.com/forgi86/pytorch-ident.git
```
in a terminal to clone the project using git. Alternatively, download the zipped project from [this link](https://github.com/forgi86/pytorch-ident/zipball/master) and extract it in a local folder
2. Install pytorch-ident by running
```
pip install .
```
in the project root folder (where the file setup.py is located).
# Bibliography
[1] M. Forgione and D. Piga. Model structures and fitting criteria for system identification with neural networks. In Proceedings of the 14th IEEE International Conference Application of Information and Communication Technologies, 2020.
[2] B. Mavkov, M. Forgione, D. Piga. Integrated Neural Networks for Nonlinear Continuous-Time System Identification. IEEE Control Systems Letters, 4(4), pp 851-856, 2020.
[3] M. Forgione and D. Piga. Continuous-time system identification with neural networks: model structures and fitting criteria. European Journal of Control, 59:68-81, 2021.
[4] M. Forgione and D. Piga. dynoNet: a neural network architecture for learning dynamical systems. International Journal of Adaptive Control and Signal Processing, 2021.
[5] D. Piga, M.Forgione and M. Mejari. Deep learning with transfer functions: new applications in system identification. In Proceedings of the the 2021 SysId Conference, 2021.
[6] G. Beintema, R. Toth and M. Schoukens. Nonlinear state-space identification using deep encoder networks. Learning for Dynamics and Control. PMLR, 2021.