System identification toolbox python

sysidentpy 0.3.1

SysIdentPy is an open-source Python module for System Identification using NARMAX models built on top of numpy and is distributed under the 3-Clause BSD license. SysIdentPy provides an easy-to-use and flexible framework for building Dynamical Nonlinear Models for time series and dynamic systems.

How do I install SysIdentPy?

The easiest way to get SysIdentPy running is to install it using pip

Requirements

A few examples require pandas >= 0.18.0. However, it is not required to use SysIdentPy.

What are the main features of SysIdentPy?

Feature	What is this?
NARMAX philosophy	You can build variations of NARMAX models like NARX, NAR, NARMA, NFIR, ARMA, ARX, AR, and others.
Model Structure Selection	Easy-to-use methods to select the best terms to build your models, including FROLS and MetaMSS and several combinations with parameter estimation techniques to select the model terms.
Basis Function	You can use different basis functions to build your models. You can set linear and nonlinear basis functions and ensemble them to get custom NARMAX models.
Parameter Estimation	More than 15 methods to estimate the model parameters and test different structure selection scenarios.
Model Simulation	You can reproduce results from papers easily with SimulateNARMAX class. Moreover, you can test published models with different parameter estimation methods and compare the performance.
Neural NARX	You can use SysIdentPy with Pytorch to create custom neural NARX models architectures which support all the optimizers and loss functions from Pytorch.
General Estimators	You can use estimators from packages like scikit-learn, Catboost, and many other compatible interfaces and composition tools, to create NARMAX models.

Why does SysIdentPy exist?

SysIdentPy aims to be a free and open source package to help the community to design NARMAX models. More than that, be a free and robust alternative to one of the most used tools to build NARMAX models, which is the Matlab’s System Identification Toolbox.

The project is actively maintained by Wilson R. L. Junior and looking for contributors.

How do I use sysIdentPy?

The SysIdentPy documentation includes 20 examples to help get you started:

• Typical «Hello World» example, for an entry-level description of the main SysIdentPy concepts
• A dedicated section focusing on SysIdentPy features, like model structure selection algorithms, basis functions, parameter estimation, and more.
• A dedicated section focusing on use cases using SysIdentPy with real world datasets. Besides, there is some brief comparisons and benchmarks against other time series tools, like Prophet, Neural Prophet, ARIMA, and more.

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Dynamical System Identification using python incorporating numerous powerful deep learning methods. (deepSI = deep System Identification)

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GerbenBeintema/deepSI

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README.md

Dynamical System Identification using python incorporating numerous powerful deep learning methods. (deepSI = deep System Identification)

The goal of deepSI is to provide a platform for the development and use of (deep) dynamical system identification methods. Furthermore, the deepSI module (i.e. toolbox) is implemented such that anyone can use it without requiring deep expert knowledge in either system identification or machine learning. Lastly, the usage of deepSI is intuitive and often requiring effectively no more than 10 lines of code as seen in the example below.

Documentation and Installation

The deepSI Documentation and Installation details are available at deepsi.readthedocs.io/en/latest.

import deepSI from matplotlib import pyplot as plt train, test = deepSI.datasets.Silverbox() # Automaticly downloaded (and cashed) the Silverbox system data # It also splitted the data into two instances of System_data plt.plot(train.y) #train.y = measured outputs (and train.u = the system inputs) plt.plot(test.y) plt.ylabel('y'); plt.xlabel('t'); plt.legend(['train','test']); plt.show()

#ARX model sys_SS_linear = deepSI.fit_systems.Sklearn_io_linear(na=2, nb=5) sys_SS_linear.fit(train) #fit the ARX data test_simulation_SS_linear = sys_SS_linear.apply_experiment(test) #Encoder method with neural networks (Beintema, et al. 2020a) sys_encoder = deepSI.fit_systems.SS_encoder(nx=4, na=10, nb=10) #batch optimization using PyTorch for back propagation. sys_encoder.fit(train_sys_data=train, val_sys_data=test[:5000], epochs=50, batch_size=256, loss_kwargs='nf':50>) test_simulation_encoder = sys_encoder.apply_experiment(test) #plotting the residuals plt.plot(test.y) plt.plot(test.y-test_simulation_SS_linear.y) plt.plot(test.y-test_simulation_encoder.y) plt.ylabel('y'); plt.xlabel('t'); plt.legend(['Measured','Simulation ARX', 'Simulation SS encoder']) plt.show()

• Numerous System Identification methods
  • Linear methods (e.g. ARX, Linear State Space)
  • Nonlinear methods (e.g. NARX, GP, SVM, Sub-space Encoder)
  • User defined identification methods

  Featured Projects utilizing deepSI

  Gerben Beintema, Roland Toth, Maarten Schoukens; Nonlinear State-Space Identification using Deep Encoder Networks; Submitted to l4dc 2021a; Github Repository, Arxiv

  Gerben Beintema, Roland Toth, Maarten Schoukens; Nonlinear State-space Model Identification from Video Data using Deep Encoders; Submitted to SYSID 2021b; Github repository, Arxiv

  deepSI is in ongoing development and anyone can contribute to any part of module.

  Feel free to contact me directly for any question or issues related to deepSI.

  Main developer: PhD candidate Gerben Beintema at the TU/e. Control Systems. g.i.beintema@tue.nl

  When citing deepSI please use

  Gerben Beintema, Roland Toth, Maarten Schoukens. Nonlinear State-Space Identification using Deep Encoder Networks; Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:241-250, 2021. Github, Published version

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  Dynamical System Identification using python incorporating numerous powerful deep learning methods. (deepSI = deep System Identification)

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