论文信息 - A Generalized Net Model Based on Fast Learning Algorithm of Unsupervised Art2 Neural Network

A Generalized Net Model Based on Fast Learning Algorithm of Unsupervised Art2 Neural Network

In this paper the fast learning algorithm of unsupervised adaptive resonance theory ART2 neural network is described. At the beginning of the process the algorithm is illustrated step by step by mathematical formulas and it is shown how individual vector changes its values during the training. The network supports clustering by using competitive learning, normalization and suppression of the noise. At the end of the process we have stable recognition clusters with values according to the vectors.

Sotir Sotirov | Todor Petkov

[1] S. Grossberg,et al. ART 2: self-organization of stable category recognition codes for analog input patterns. , 1987, Applied optics.

[2] Laurene V. Fausett,et al. Fundamentals Of Neural Networks , 1994 .

[3] Krassimir T. Atanassov,et al. Generalized nets , 1991 .

[4] GrossbergS.. Adaptive pattern classification and universal recoding , 1976 .

[5] S. Sumathi,et al. Introduction to neural networks using MATLAB 6.0 , 2006 .

[6] Stephen Grossberg,et al. Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.

[7] Stephen Grossberg,et al. The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[8] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.