Kohonen Self Organizing Feature Maps (SOFM) for Simulink.
This model contains a implementation of the SOFM algorithm using Simulink's basic blocks. The SOFM algorithm is associated with a single block with various configuration parameters:
Number of the neuron inputs
Grid size (rows and columns)
Initial value of standard deviation (sigma0) - Topological neighborhood function
Time constant (t1) - Topological neighborhood function decrease
Initial value of the learning-rate parameter (mu0)
Time constant (t2) - Learning-rate parameter decrease
The attached file contains an example of a network with two dimensional lattice driven by a two dimensional distribution with 100 neurons arranged in a 2D lattice of 10 x 10 nodes.
Marcelo Augusto Costa Fernandes
DCA - CT - UFRN
mfernandes@dca.ufrn.br
Cite As
Marcelo Fernandes (2024). Kohonen Self Organizing Feature Maps (SOFM) for Simulink. (https://www.mathworks.com/matlabcentral/fileexchange/36369-kohonen-self-organizing-feature-maps-sofm-for-simulink), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
Windows macOS LinuxCategories
- AI, Data Science, and Statistics > Deep Learning Toolbox > Function Approximation, Clustering, and Control >
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.