The network topology is defined by the number of neurons. The learning algorithm of the Kohonen Network uses euclidean distance as a metric
- Step1: Load input vector
- Step2: Get best matching unit by calculating the minimal distance between a node's weight vector and the input vector
- Step 3: The winning node learns the data (weight modification)
- Step 4: Modify the neighbours weights(the learning fades as a gaussian func)
- Repeat until number of iteration reached (no more learning vectors)
This library is made to communicate with the OMNet++ simulator via shared files. The network learns the data exchanged within a wireless sensor network for classifying. It can be used for other purpose if ùodified a little bit.
Copyright (c) 2016, Asma DHANE