ANN – Implementation of Self Organizing Neural Network (SONN) from Scratch
To implement a SONN, here are some essential consideration-
- Construct a Self Organizing Neural Network (SONN) or Kohonen Network with 100 neurons arranged in a 2-dimensional matrix with 10 rows and 10 columns
- Train the network with 1500 2-dimensional input vectors randomly generated in the interval between -1 and +1
- Select initial synaptic weights randomly in the same interval -1 and +1
- Assign learning rate parameter is equal to 0.1
- Objective is to classify 2-dimensional input vectors such that each neuron in the network should respond only to the input vectors occurring in its region
- Test the performance of the self organizing neurons using the following Input vectors:
Python Implementation of SONN:
All the 10×10 neurons are represented by green color. The nearest neuron which is activated by the test input is shown with red color. Here, the euclidean distances between activated neurons and test inputs are also shown in these plots. Respective neuron will respond for each of the test input vector samples.
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