https://github.com/anselmoo/rbf_networkfitting

Radial-Basis-Function-Network for solving the 1D- and 2D-minimization problem
https://github.com/anselmoo/rbf_networkfitting

fitting-algorithm genetic-algorithm neural-network python spectroscopy

Last synced: 2 days ago
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Radial-Basis-Function-Network for solving the 1D- and 2D-minimization problem

• Host: GitHub
• URL: https://github.com/anselmoo/rbf_networkfitting
• Owner: Anselmoo
• License: mit
• Created: 2019-09-13T13:59:24.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-08-14T17:28:41.000Z (almost 5 years ago)
• Last Synced: 2025-06-20T17:06:23.394Z (2 days ago)
• Topics: fitting-algorithm, genetic-algorithm, neural-network, python, spectroscopy
• Language: Python
• Homepage:
• Size: 7.82 MB
• Stars: 6
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# RBF Network Fitting

**RBF Network Fitting** is an in Python developed fitting routine, which is using the [Radial-Basis-Function-Network for solving](https://en.wikipedia.org/wiki/Radial_basis_function_network) the 1D- and 2D-minimization problem. During the *Self-Consistent-Field-Optimization* of the RBF-Network, the `mean-squared-error` will be evaluated for each cycle, and a *difference- and gradient-correction* will be applied to the input-parameter of the Fitting-Model. As Fitting-Models can be choosen: 

 * [Normal Distribution](https://en.wikipedia.org/wiki/Normal_distribution)

 * [Cauchy/Lorentzian Distribution](https://en.wikipedia.org/wiki/Cauchy_distribution)

 * [Pseudo-Voigt Profile](https://en.wikipedia.org/wiki/Voigt_profile#Pseudo-Voigt_Approximation)

In order to optimize the *Hyperparameter-Finding* for the number of layers and the kind of choosen models, a [Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) can be optionally used. The combination of both *Radial-Basis-Function-Network* and *Genetic Algorithm* allows using **RBF Network Fitting** as a real `black-box-method` in the absence of empirical parameters.

## Examples

* Detecting peaks of an oscillating function

Example - I             |  Example - II

:-------------------------:|:-------------------------:

![osci_1](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/docu/example_2.png)|![osci_2](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/docu/example_6.png)

* Fitting of experimental data

Example - III             |  

:-------------------------:|

![d6_example](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/docu/example_5.png)|

* Following patterns of 3D-Functions

Example - IV             |  Example - V

:-------------------------:|:-------------------------:

![3D-I](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/docu/example_4.png)|![3D-II](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/docu/example_7.png)

**RBF Network Fitting** requires:

  * [numpy](https://github.com/numpy/numpy)

  * [matplotlib](https://github.com/matplotlib/matplotlib)

  

 

 Installing and Running:

```python 

python setup.py install

# as command line application 

python -m RBFN 

# as library

from RBFN import GeneticFitter

from RBFN import RBFNetwork

from RBFN import PlotResults

```

## Further Readings:

```

Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions

Frances Buontempo

Pragmatic Bookshelf, 2019

```

```

Genetic Algorithms with Python

Clinton Sheppard

Clinton Sheppard, 2018

```

[https://github.com/handcraftsman/GeneticAlgorithmsWithPython/blob/master/ch08/genetic.py](https://github.com/handcraftsman/GeneticAlgorithmsWithPython/blob/master/ch08/genetic.py)

[https://en.wikipedia.org/wiki/Radial_basis_function_network](https://en.wikipedia.org/wiki/Radial_basis_function_network)