Abstract
In this paper, an unsupervised learning algorithm is developed. Two versions of an artificial neural network, termed a differentiator, are described. It is shown that our algorithm is a dynamic variation of the competitive learning found in most unsupervised learning systems. These systems are frequently used for solving certain pattern recognition tasks such as pattern classification and k-means clustering. Using computer simulation, it is shown that dynamic competitive learning outperforms simple competitive learning methods in solving cluster detection and centroid estimation problems. The simulation results demonstrate that high quality clusters are detected by our method in a short training time. Either a distortion function or the minimum spanning tree method of clustering is used to verify the clustering results. By taking full advantage of all the information presented in the course of training in the differentiator, we demonstrate a powerful adaptive system capable of learning continuously changing patterns.
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References
Ahalt SC, Krishnamurthy AK, Chen P, Melton D (1990) Competitive learning algorithms for vector quantization. Neural Networks 3:277–290
Chinrungrueng C, Carlo HS (1991) Optimal adaptive k-means algorithm with dynamic adjustment of learning rate. In: Proceedings of the international joint conference on neural networks (IJCNN91), Seattle, Vol 1, pp 855–862
Darken C, Moody J (1990) Fast adaptive k-means clustering: some empirical results. In: Proceedings of the international joint conference on neural networks (IJCNN-90). pp 233–238
Decaestecker C (1990) Concept discovery by competitive clustering. In: Proceedings of IEEE international conference on neural networks, Paris, 1990
Garis H (1989) ‘COMPO’ Conceptual clustering with connectionist competitive learning. First IEEE international conference on artificial neural networks, IEE, Savoy Place, London, pp 226–232
Grossberg S (1987) Competitive learning: from interactive activation to adaptive resonance. Cogn Sci 11:2363
Hartigan J (1975) Clustering algorithms. Wiley, New York
Hecht-Nielsen R, Reading, MA (1990) Neurocomputing. Addison-Wesley.
Kandel ER (1979) Small systems of neurons. Sci Am 3:61–70
Kia SJ, Coghill GG (1991a) A mapping neural network using unsupervised and supervised training. In: Proceedings of the international joint conference on neural networks (IJCNN91), Seattle, Vol 2, pp 587–590
Kia SJ, Coghill GG (1991b) Dynamic competitive learning for centroid estimation. In: Proceedings of the international joint conference on neural networks (IJCNN91), Singapore, pp 857–862
Kia SJ, Coghill GG (1991c) Dynamic competitive learning in the differentiator. In: Proceedings of 1991 international symposium on circuits and systems, Singapore, pp 1489–1492
Kia SJ, Coghill GG (1991d) An artificial neural network for cluster detection using competitive learning. In: Proceedings of the second Australian conference on neural networks, Sydney, pp 189–193
Kohonen T (1989) Self organization and associative memory. Springer, Third Edition
Kong S, Kosko B (1991) Differential competitive learning for centroid estimation and phoneme recognition. IEEE Transactions on neural networks 1:118–124
Malsburg C (1973) Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14:85–100
Ng EW, Coghill GG, Tuck DL (1991) Tactile robot shape recognition using geometrical angle length sequences. In: Proceedings of the international joint conference on neural networks (IJCNN-91), Singapore, pp 307–312
Nowlan SJ (1990) Maximum likelihood competitive learning. In: Touretzky DS, editor, Advances in neural information processing systems 2, Morgan Kaufmann, pp 574–582
Rumelhart DE, Zipser D (1985) Feature discovery by competitive learning. Cogn Sci 9:75–112
Sayers CP, Coghill GG (1989) A continuously adaptable artificial neural network. First IEE international conference on artificial neural networks, IEEE, Savoy Place, London, pp 351–355
Tavan P, Grubmuller H, Kuhnel H (1990) Self-organization of associative memory and pattern classification: recurrent signal processing on topological feature maps. Biol Cybern 64:165–170
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Kia, S.J., Coghill, G.G. Unsupervised clustering and centroid estimation using dynamic competitive learning. Biol. Cybern. 67, 433–443 (1992). https://doi.org/10.1007/BF00200987
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DOI: https://doi.org/10.1007/BF00200987