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.
 Saadati Moghaddam, Goodarz; Ali Naseri and Sayyed Hassanollah Asadollahi, Intelligent Algorithm for Identifying Radar Signals Using Matrix Multiplication and RBF Neural Network, 13th Iranian Student Electrical Engineering Conference, Tarbiat Modares University, Tehran,2010
 Fathi, Abdolhossein and Shima Shafiei, An Approach to Machine Learning Algorithms for Artificial Neural Network, MLP Neural Network, RBF Neural Network, Third National Conference on Electrical and Computer Engineering Technology, Tehran, Payam Noor University and Payam Noor University of Tehran,2016
 Ghasemi, Mohammad Reza and Masoud Dadgar, LVQ Presentation of a Method for Algorithmic Classification of Data, 2nd Iranian National Congress on New Technologies for Sustainable Development, Tehran, Mehr Arvand Institute of Sustainable Development,2014
 J. A. Anderson, M. T. Gately, P. A. Penz, and D. R. Collins, “Radar signal categorization using a neural network,” Proceedings of the IEEE, vol. 78, no. 10, pp. 1646–1657, 1990.
 A. M. Aziz, “A novel and efficient approach for automatic classification of radar emitter signals,” in 2013 IEEE Aerospace Conference, 2013, pp. 1–8.
 B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” in Proceedings of the fifth annual workshop on Computational learning theory - COLT ’92, 1992, pp. 144–152.
 M. Cherniakov, R. S. A. R. Abdullah, P. Jancovic, M. Salous, and V. Chapursky, “Automatic ground target classification using forward scattering radar,” Radar, Sonar and Navigation, IEE Proceedings, vol. 153, no. 5, pp. 427–437, Oct. 2006.
 C. L. Davies and P. Hollands, “Automatic processing for ESM,” IEE Proceedings F Communications, Radar and Signal Processing, vol. 129, no. 3, p. 164, Jun. 1982.
 J. Dudczyk, A. Kawalec, and J. Cyrek, “Applying the distance and similarity functions to radar signals identification,” in 2008 International Radar Symposium, 2008, pp. 1–4.
 P. M. Grant and J. H. Collins, “Introduction to electronic warfare,” IEE Proceedings F Communications, Radar and Signal Processing, vol. 129, no. 3, p. 113, Jun. 1982.
 J. Han, M. Kamber, and J. Pei, Data Mining Concepts And Techniques, Third Edit. Morgan Kaufmann Publisher, 2012, p. 740.
 E.J. Hartman, J.D. Keeler, J.M Kowalski, “Layered neural networks with Gaussian hidden units as universal approximations,” Neural computation, 1990. 2(2): p. 210-215.
 J. Liu, J. Lee, L. Li, Z.Q. Luo, and K.M. Wong, “On-line Clustering Algorithms for Radar Emitter Classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. 27, No. 8, pp. 1185–1196, Aug. 2005
 K. Pearson, “On lines and planes of closest fit to systems of points in space,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1991. 2(11): p. 559-572
 M.-Q. Ren, Y. Zhu, Y. Mao, and J. Han, “Radar emitter signals classification using kernel principle component analysis and fuzzy support vector machines,” in 2007 International Conference on Wavelet Analysis and Pattern Recognition, 2007, vol. 3, pp. 1442–1446.
 E. Świercz, “Automatic Classification of LFM Signals for Radar Emitter Recognition Using Wavelet Decomposition and LVQ Classifier,” vol. 119, no. 4, pp. 488–494, 2011.
 V. Vapnik, The Nature of Statistical Learning Theory. Springer Science & Business Media, 2000, p. 314.
 R. G. Wiley, Electronic Intelligence: The Analysis of Radar Signals. Artech House, 1993, p. 337.
 Bhattacharya, Gautam, Koushik Ghosh, and Ananda S. Chowdhury. "An affinity-based new local distance function and similarity measure for kNN algorithm." Pattern Recognition Letters 33.3 (2012): 356-363.