TY - JOUR
AU - Saeed Talati
AU - Mohammadreza Hassani Ahangar
PY - 2020/03/01
Y2 - 2020/04/07
TI - Analysis, Simulation and Optimization of LVQ Neural Network Algorithm and Comparison with SOM
JF - Majlesi Journal of Telecommunication Devices
JA - MJTD
VL - 9
IS - 1
SE - Articles
DO -
UR - http://journals.iaumajlesi.ac.ir/td/index/index.php/td/article/view/576
AB - The neural network learning vector quantization can be understood as a special case of an artificial neural network, more precisely, a learning-based approach - winner takes all. In this paper, we investigate this algorithm and find that this algorithm is a supervised version of the vector quantization algorithm, which should check which input belongs to the class (to update) and improve it according to the distance and class in question. To give. A common problem with other neural network algorithms is the speed vector learning algorithm, which has twice the speed of synchronous updating, which performs better where we need fast enough. The simulation results show the same problem and it is shown that in MATLAB software the learning vector quantization simulation speed is higher than the self-organized neural network.
ER -