Efficient methods of initializing neuron weights in self-organizing networks implemented in hardware

The investigations have shown that Self-Organizing Maps (SOMs) in many situations may be trained without any initialization (with zeroed weights). This is possible due to the neighborhood mechanism that to some degree stimulates the neurons belonging to the SOM. We present selected results of several thousands simulations for different topologies of the SOM, for different neighborhood functions and two distance measures between the learning patterns and neurons in the input data space. Simulations were performed for initial values of the weights equal to zero, for small values (up to 1% of full scale range) and for neurons randomly distributed over the overall input data space. The results in most cases are comparable that allows to reduce the complexity of the SOM implemented in the CMOS technology.