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NeurApp

  NeurApp is an intuitive software for exploring approximation by artificial neural networks. It can be freely downloaded from the internet.


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About the Software

  The simple and intuitive software has been created by Igor Grešovnik and Tadej Kodelja. It is aimed at exploring properties and function of modelling by artificial neural networks. The software is based on the Investigative Generic Library (IGLib) and uses the Aforge.Net  neural network library. See the User Manual and the Documentation Section for more details.


Documentation

  NeurApp User Manual. PDF document containing instructions for installation, running, and using the software, and some background information about the software. Also available as html page and mhtml document.

  NeurApp License Agreement. Contains terms of use of the software.

  ReadMe file. Contains very basic information about the software.

  NeurApp Code Documentation (base index). Code documentation for the software, automatically generated from documentation comments by Doxygen. Only the top-level part of code and the most relevant libraries are documented here. For additional documentation of base libraries, see the IGLib Code documentation.


  See also:

Free Download

   NeurApp can be downloaded here and can be used free of charge by anyone (see the license agreement). The current version is 1.0.

Download NeurApp (click on the link) or 32-bit version for older (32 bit) Windows systems.

Short installation instructions:

System Requirements


  The application requires the 64 bit .NET framework or its alternative implementation such as Mono. One of these is available for most modern computing platforms and are many times pre-installed with the operating system. If not yet installed, you will have to download and install the .NET framework (for Widows operating systems) or Mono (cross platform) before using the software.
  There may be problems with using the software on some older platforms, e.g. Windows XP. For more details, see the System Requirements Section at the IGLib pages.

Examples and Screenshots



DUI for 1D approximation
Approximation of function of 1 variable.

sampled data for approximation of a function of 2 variables
Generating training data by sampling a user defined reference function of two variables.


sampled data for approximation of a function of 2 variables
Neural network - based model of a user defined function of two variables.Original function is shown for comparison, with errors shown in color scale.

detail - comparison of original function and ANN model
Zoomed-in detail of an area with larger approximation errors.

Model with contours.
Plot of approximated data with contours shown.

Fine tuning display options.
There are a number of display options that make inspection of the approximation and its errors really easy and clear. One can individually switch on or off display of training points, grid, surfaces and contours of the original function and its approximation. Transparency of both surfaces can be continuously adjusted. In this figure, both original and approximated surface were drawn relatively opaque, therefore it is immediately clear where approximated function has higher or lower values than the original one. Contours of the original function are displayed, which gives better feeling of 3D shapes.




Previous Versions


  This software is available as freeware and is freely available on these pages. Different version can be downloaded below. For download and installation instructions, see the Download Section.


Version 1.0



Version 1.1











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  Updated in April 2015  



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