Initialization mechanism in Kohonen neural network implemented in CMOS technology
نویسندگان
چکیده
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.
منابع مشابه
MIXDES 2008 Proceedings
In this paper, we present an experimental current-mode Kohonen neural network (KNN) implemented in a CMOS 0.18 m process. The network contains four output neurons. Each neuron has three analog weights related to three inputs. The presented KNN has been realized using building blocks proposed earlier by the authors, such as binary tree current-mode winner takes all (WTA) circuit, Euclidean dista...
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