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System identification tools in PyTorch\nA collection of system identification tools implemented in PyTorch.\n\n* State-space identification methods (see [1], [2], [3], [6])\n* Differentiable transfer functions (see [4], [5])\n\n## Examples and Documentation\n\n* Examples are provided in the [**examples**](examples) folder of this repo.\n* The API documentation is available at https://pytorch-ident.readthedocs.io/en/latest.\n\n\n## Installation:\n\n### Requirements:\nA Python 3.9 conda environment with\n\n * numpy\n * scipy\n * matplotlib\n * pandas\n * pytorch\n \n### Stable version from PyPI\n\nRun the command \n\n```\npip install pytorch-ident\n```\nThis will install the current [stable version](https://pypi.org/project/pytorch-ident/) from the PyPI package repository.\n\n### Latest version from GitHub\n1. Get a local copy the project. For instance, run \n```\ngit clone https://github.com/forgi86/pytorch-ident.git\n```\nin 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\n\n2. Install pytorch-ident by running\n```\npip install .\n```\nin the project root folder (where the file setup.py is located). \n\n# Bibliography\n[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. \u003cbr/\u003e\u003cbr/\u003e\n[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. \u003cbr/\u003e\u003cbr/\u003e\n[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. \u003cbr/\u003e\u003cbr/\u003e\n[4] M. Forgione and D. Piga. dynoNet: a neural network architecture for learning dynamical systems. International Journal of Adaptive Control and Signal Processing, 2021. \u003cbr/\u003e\u003cbr/\u003e\n[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. \u003cbr/\u003e\u003cbr/\u003e\n[6] G. Beintema, R. Toth and M. Schoukens. Nonlinear state-space identification using deep encoder networks. Learning for Dynamics and Control. PMLR, 2021. \u003cbr/\u003e\u003cbr/\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fforgi86%2Fpytorch-ident","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fforgi86%2Fpytorch-ident","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fforgi86%2Fpytorch-ident/lists"}