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python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]

steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
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30 changes: 30 additions & 0 deletions CHANGELOG
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Changes in SysIdentPy
=====================

v0.3.0
------

CONTRIBUTORS
~~~~~~~~~~~~

- wilsonrljr
- gamcorn
- Gabo-Tor

CHANGES
~~~~~~~

- The update **v0.3.3** has been released with additional features, API changes and fixes.

- MAJOR: Multiobjective Framework: Affine Information Least Squares Algorithm (AILS)
- Now you can use AILS to estimate parameters of NARMAX models (and variants) using a multiobjective approach.
- AILS can be accessed using `from sysidentpy.multiobjective_parameter_estimation import AILS`
- See the docs for a more in depth explanation of how to use AILS.
- This feature is related to Issue #101. This work is the result of an undergraduate research conducted by Gabriel Bueno Leandro under the supervision of Samir Milani Martins and Wilson Rocha Lacerda Junior.

- API Change: `regressor_code` variable was renamed as `enconding` to avoid using the same name as the method in `narmax_tool` `regressor_code` method.

- DATASET: Added buck_id.csv and buck_valid.csv dataset to SysIdentPy repository.

- DOC: Add a Multiobjetive Parameter Optimization Notebook showing how to use the new AILS method

- DOC: Minor additions and grammar fixes.


v0.3.2
------

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Expand Up @@ -26,4 +26,8 @@ In addition to these resources, we will also reference Luis Antônio Aguirre `In

## Contribute

The `Nonlinear Dynamics: A Journey Through System Identification and Forecasting` is a comprehensive resource on the science of System Identification, offered as an open-source material. Our aim is to make this valuable resource accessible to all, both financially and intellectually. If you have found this book helpful and want to support our endeavor financially, you are referred to the Sponsor page. However, if you are not yet ready to contribute financially, you can still help us by pointing out typos, suggesting edits, or offering feedback on passages that you found difficult to comprehend. Simply navigate to the book's repository and open an issue. Lastly, if you enjoyed our content, please consider sharing it with others who may find it useful and leave us a star on GitHub.
The `Nonlinear Dynamics: A Journey Through System Identification and Forecasting` is a comprehensive resource on the science of System Identification, offered as an open-source material. Our aim is to make this valuable resource accessible to all, both financially and intellectually. If you have found this book helpful and want to support our endeavor financially, you are referred to the Sponsor page. However, if you are not yet ready to contribute financially, you can still help us by pointing out typos, suggesting edits, or offering feedback on passages that you found difficult to comprehend. Simply navigate to the book's repository and open an issue. Lastly, if you enjoyed our content, please consider sharing it with others who may find it useful and leave us a star on GitHub.

## Note

The chapters will be released one by one until the book is complete.
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Expand Up @@ -26,4 +26,8 @@ In addition to these resources, we will also reference Luis Antônio Aguirre `In

## Contribute

The `Nonlinear Dynamics: A Journey Through System Identification and Forecasting` is a comprehensive resource on the science of System Identification, offered as an open-source material. Our aim is to make this valuable resource accessible to all, both financially and intellectually. If you have found this book helpful and want to support our endeavor financially, you are referred to the Sponsor page. However, if you are not yet ready to contribute financially, you can still help us by pointing out typos, suggesting edits, or offering feedback on passages that you found difficult to comprehend. Simply navigate to the book's repository and open an issue. Lastly, if you enjoyed our content, please consider sharing it with others who may find it useful and leave us a star on GitHub.
The `Nonlinear Dynamics: A Journey Through System Identification and Forecasting` is a comprehensive resource on the science of System Identification, offered as an open-source material. Our aim is to make this valuable resource accessible to all, both financially and intellectually. If you have found this book helpful and want to support our endeavor financially, you are referred to the Sponsor page. However, if you are not yet ready to contribute financially, you can still help us by pointing out typos, suggesting edits, or offering feedback on passages that you found difficult to comprehend. Simply navigate to the book's repository and open an issue. Lastly, if you enjoyed our content, please consider sharing it with others who may find it useful and leave us a star on GitHub.

## Note

The chapters will be released one by one until the book is complete.
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# Changes in SysIdentPy

## v0.3.3

### CONTRIBUTORS

- wilsonrljr
- GabrielBuenoLeandro
- samirmartins

### CHANGES

- The update **v0.3.3** has been released with additional features, API changes and fixes.

- MAJOR: Multiobjective Framework: Affine Information Least Squares Algorithm (AILS)
- Now you can use AILS to estimate parameters of NARMAX models (and variants) using a multiobjective approach.
- AILS can be accessed using `from sysidentpy.multiobjective_parameter_estimation import AILS`
- See the docs for a more in depth explanation of how to use AILS.
- This feature is related to Issue #101. This work is the result of an undergraduate research conducted by Gabriel Bueno Leandro under the supervision of Samir Milani Martins and Wilson Rocha Lacerda Junior.
- Several new methods were implemented to get the new feature and you can check all of it in sysidentpy -> multiobjective_parameter_estimation.

- API Change: `regressor_code` variable was renamed as `enconding` to avoid using the same name as the method in `narmax_tool` `regressor_code` method.

- DATASET: Added buck_id.csv and buck_valid.csv dataset to SysIdentPy repository.

- DOC: Add a Multiobjetive Parameter Optimization Notebook showing how to use the new AILS method

- DOC: Minor additions and grammar fixes.

## v0.3.2

### CONTRIBUTORS
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# Changes in SysIdentPy

## v0.3.3

### CONTRIBUTORS

- wilsonrljr
- GabrielBuenoLeandro
- samirmartins

### CHANGES

- The update **v0.3.3** has been released with additional features, API changes and fixes.

- MAJOR: Multiobjective Framework: Affine Information Least Squares Algorithm (AILS)
- Now you can use AILS to estimate parameters of NARMAX models (and variants) using a multiobjective approach.
- AILS can be accessed using `from sysidentpy.multiobjective_parameter_estimation import AILS`
- See the docs for a more in depth explanation of how to use AILS.
- This feature is related to Issue #101. This work is the result of an undergraduate research conducted by Gabriel Bueno Leandro under the supervision of Samir Milani Martins and Wilson Rocha Lacerda Junior.
- Several new methods were implemented to get the new feature and you can check all of it in sysidentpy -> multiobjective_parameter_estimation.

- API Change: `regressor_code` variable was renamed as `enconding` to avoid using the same name as the method in `narmax_tool` `regressor_code` method.

- DATASET: Added buck_id.csv and buck_valid.csv dataset to SysIdentPy repository.

- DOC: Add a Multiobjetive Parameter Optimization Notebook showing how to use the new AILS method

- DOC: Minor additions and grammar fixes.

## v0.3.2

### CONTRIBUTORS
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# Documentation for `Multiobjective Parameter Estimation`

::: sysidentpy.multiobjective_parameter_estimation.estimators
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# Documentation for `Multiobjective Parameter Estimation`

::: sysidentpy.multiobjective_parameter_estimation.estimators
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<link href="https://sysidentpy.org/" rel="canonical" />
<link href="landing-page/getting-started/" rel="next" />
<link href="overrides/assets/images/favicon.png" rel="icon" />
<meta content="mkdocs-1.4.3, mkdocs-material-9.1.11" name="generator" />
<meta content="mkdocs-1.5.3, mkdocs-material-9.1.11" name="generator" />
<title>SysIdentPy - SysIdentPy</title>
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<link href="assets/stylesheets/palette.a6bdf11c.min.css" rel="stylesheet" />
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<li class="md-nav__item"> <a class="md-nav__link"
href="examples/parameter_estimation/"> Parameter
Estimation </a> </li>
<li class="md-nav__item"> <a class="md-nav__link"
href="examples/multiobjective_parameter_estimation/">
Multiobjective Parameter Estimation for NARMAX
models - An Overview </a> </li>
<li class="md-nav__item"> <a class="md-nav__link"
href="examples/extended_least_squares/"> Extended
Least Squares </a> </li>
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<li class="md-nav__item"> <a class="md-nav__link"
href="code/parameter-estimation/"> Parameter Estimation </a>
</li>
<li class="md-nav__item"> <a class="md-nav__link"
href="code/multiobjective-parameter-estimation/"> Multiobjective
Parameter Estimation </a> </li>
<li class="md-nav__item"> <a class="md-nav__link"
href="code/simulation/"> Simulation </a> </li>
<li class="md-nav__item"> <a class="md-nav__link" href="code/residues/">
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