Competitive learning methods with refractory and creative approaches

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

Volumn E82-A, Issue 9, 1999, Pages 1825-1832

  Maeda, Michiharu ^a   Miyajima, Hiromi ^b  

^a KURUME NATIONAL COLLEGE OF TECHNOLOGY   (Japan)

^b KAGOSHIMA UNIVERSITY   (Japan)

Author keywords

Competitive learning; Creating mechanism; Neural networks; Refractory period; Vector quantization

Indexed keywords

COMPETITIVE LEARNING;

NEURAL NETWORKS; VECTOR QUANTIZATION;

LEARNING ALGORITHMS;

EID: 0033184716     PISSN: 09168508     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. S. Grossberg, Adaptive pattern classification and universal receding: I. Parallel development and coding of neural feature detectors, Biol. Cybern., vol.23, pp.121-134, 1976.
2. D.J. Willshaw and C. von der Malsburg, How patterned neural connections can be set up by self-organization, Proc. R. Soc. Lond. B., vol.194, pp.431-445, 1976.
3. J. Hertz, A. Krogh, and R.G. Palmer, Introduction to the theory of neural computation, Addison-Wesley, 1991.
4. Y. Linde, A. Buzo, and R.M. Gray, An algorithm for vector quantizer design, IEEE Trans. Commun., vol.28, pp.84-95, 1980.
5. T. Kohonen, Self-organization and associative memory, Springer-Verlag, Berlin, 1989.
6. D.-I. Choi and S.-H. Park, Self-creating and organizing neural networks, IEEE Trans. Neural Networks, vol.5, pp.561-575, 1994.
7. T.M. Martinetz, S.G. Berkovich, and K.J. Schulten, Neural-gas network for vector quantization and its application to time-series prediction, IEEE Trans. Neural Networks, vol.4, pp.558-569, 1993.
8. M. Maeda, H. Miyajima, and S. Murashima, An adaptive learning and self-deleting neural network for vector quantization, IEICE Trans. Fundamentals, vol.E79-A, no.11, pp. 1886-1893, Nov. 1996.
9. M. Maeda, H. Miyajima, and S. Murashima, Construction of self-organizing algorithms for vector quantization, T.IEE Japan, vol.117-C, pp.1084-1091, 1997.
10. M. Maeda, H. Miyajima, and T. Takahashi, Creative and competitive learning methods for vector quantization, IEICE Proc. Int. Symp. Nonlinear Theory and its Applications, vol.2, pp.771-774, 1997.
11. N. Ueda and R. Nakano, Competitive and selective learning method for vector quantizer design: Equidistortion principle and its algorithm, IEICE Trans., vol.J77-D-II, no.11, pp.2265-2278, Nov. 1994.
12. Y. Yamada, S. Tazaki, and R. Gray, Asymptotic performance of block quantizers with difference distortion measures, IEEE Trans. Inform. Theory, vol.26, pp.6-14, 1980.
13. P.L. Zador, Asymptotic quantization error of continuous signals and the quantization dimension, IEEE Trans. Inform. Theory, vol.28, pp. 139-149, 1982.
14. H. Ritter and K. Schulten, On the stationary state of Kohonen's self-organizing sensory mapping, Biol. Cybern., vol.54, pp.99-106, 1986.
15. H. Ritter and K. Schulten, Convergence properties of Kohonen's topology conserving maps: Fluctuations, stability, and dimension selection, Biol. Cybern., vol.60, pp.59-71, 1988.
16. T. Tanaka and M. Saito, Quantitative properties of Kohonen's self-organizing maps as adaptive vector quantizers, IEICE Trans., vol.J75-D-II, no.6, pp.1085-1092, Jun. 1992.
17. L.E. Reichl, A modern course in statistical physics, University of Texas Press, 1980.
18. H. Imai, Information theory, Shokodo, 1984.
19. S.C. Ahalt, A.K. Krishnamurthy, P. Chen, and D. Melton, Competitive learning algorithm for vector quantization, Neural Networks, vol.3, pp.227-290, 1990.
20. K. Hotta, A. Iwata, H. Matsuo, and N. Suzumura, A large scale neural network CombNET-II, IEICE Trans., Vol.J75-D-II, no.3, pp.545-553, March 1992.