Initialization Mechanism in Kohonen Neural Network Implemented in CMOS Technology

An initialization mechanism is presented for Kohonen neural network implemented in CMOS technology. Proper selection of initial values of neurons’ weights has a large influence on speed of the learning algorithm and finally on the quantization error of the network, which for different initial parameters can vary even by several orders of magnitude. Experiments with the software model of designed network show that results can be additionally improved when conscience mechanism is used during the learning phase. This mechanism additionally decreases number of dead neurons, which minimizes the quantization error. The initialization mechanism together with experimental Kohonen neural network with four neurons and 3 inputs have been designed in CMOS 0.18 μm technology.

Published in:
proc. of the European Symposium on Artificial Neural Networks (ESANN)
Presented at:
European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, April 23-25, 2008.

Note: The status of this file is: EPFL only

 Record created 2010-08-17, last modified 2018-03-17

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