Analysis, Simulation and Optimization of LVQ Neural Network Algorithm and Comparison with SOM
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.
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Detection of Steganography in LPC, CELP and MELP Audio Standards Encoder Using LVQ Neural Network
Pouriya Etezadifar *, , Mohammadreza Hassani Ahangar, Mahdi Mollazade
Journal of Researches on Rlectronic Defense Systems, -
Presenting a New Model of Optimal Coordinated beam former Vector Selection in DRFM for Radar Jamming
Hasan Mohammadi, Khodadad Halili, Vahidreza Soltaninia, Meysam Bayat, *
Majlesi Journal of Telecommunication Devices, Sep 2023