Abstract
We propose a new self-organizing neural model that considers a dynamic topology among neurons. This leads to greater plasticity with respect to the self-organizing neural network (SOFM). Theorems are presented and proved that ensure the stability of the network and its ability to represent the input distribution. Finally, simulation results are shown to demonstrate the performance of the model, with an application to colour image compression.
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Corridoni, J. M., Del Bimbo A. and Landi, L.: 3D Object classification using multi-object Kohonen networks, Pattern Recognition, 29 (1996), 919–935.
Kohonen, T.: The self-organizing map Proc. IEEE, 78 (1990), 1464–1480.
Kohonen, T.: Self-Organizing Maps, Springer Verlag: Berlin, Germany, 2001.
Pham, D. T. and Bayro-Corrochano, E. J.: Self-organizing neural-network-based pattern clustering method with fuzzy outputs' Pattern Recognition, 27 (1994), 1103–1110.
Subba Reddy, N. V. and Nagabhushan, P.: A three-dimensional neural network model for unconstrained handwritten numeral recognition: a new approach, Pattern Recognition, 31 (1998), 511–516.
Von der Malsburg, C.: Network self-organization, in S. F. Zornetzer, J. L. Davis and C. Lau (Eds), An Introduction to Neural and Electronic Networks, Academic Press, Inc.: San Diego, CA, 1990, pp. 421–432.
Wang, S. S. and Lin, W. G.: A new self-organizing neural model for invariant pattern recognition. Pattern Recognition, 29 (1996), 677–687.
Gray, R. M.: Vector Quantization, IEEE ASSP Magazine, 1 (1980), 4–29.
Ahalt, S. C., Krishnamurphy, A. K., Chen, P. and Melton, D. E.: Competitive learning algorithms for vector quantization. Neural Networks, 3 (1990), 277–290.
Yair, E., Zeger, K. and Gersho, A.: Competitive learning and soft competition for vector quantizer design. IEEE Trans. Signal Processing, 40(2) (1992), 294–308
Ueda, N. and Nakano, R.: A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers. Neural Networks, 7(8) (1994), 1211–1227.
Linde, Y., Buzo, A. and Gray, R. M.: An algorithm for vector quantizer design. IEEE Trans. Communications, 28(1) (1980), 84–95.
Dony, R. D. and Haykin, S.: Neural networks approaches to image compression. Proc. IEEE, 83(2) (1995), 288–303.
Cramer, C.: Neural networks for image and video compression: a review, European Journal of Operational Research, 108 (1998), 266–282.
Comstock, D. and Gobson, J.: Hamming coding of DCT compressed images over noisy channels. IEEE Trans. Communications, 32 (1984), 856–861.
Wyner, A. D. and Ziv, J.: The sliding-window Lempel-Ziv algorithm is asymptotically optimal. Proc. IEEE, 82 (1994), 872–877.
Wyner, A. D. and Wyner, A. J.: Improved redundancy of a version of the Lempel-Ziv algorithm. IEEE Trans. Information Theory, 35 (1995), 723–731.
Mc Donald, D.: Homo sapiens karotypes [online]. Date of access: August 2001. Available at: http://www.selu.com/bio/cyto/karyotypes/Hominidae/Hominidae.html
Van Hulle, M. M.: Faithful representations with topographic maps. Neural Networks, 12(6) (1999), 803–823.
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López-Rubio, E., Muñoz-Pérez, J. & Antonio Gómez-Ruiz, J. Self-Organizing Dynamic Graphs. Neural Processing Letters 16, 93–109 (2002). https://doi.org/10.1023/A:1019999727252
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DOI: https://doi.org/10.1023/A:1019999727252