Abstract
In this paper, we propose a Chaotic Complex-Valued Associative Memory with Adaptive Scaling Factor which can realize dynamic association of multi-valued pattern. In the proposed model, the scaling factor of refractoriness is adjusted according to the maximum absolute value of the internal state up to that time as similar as the conventional Chaotic Associative Memory with Adaptive Scaling Factor. Computer experiments are carried out and we confirmed that the proposed model has the same dynamic association ability as the conventional model, and the proposed model also has recall capability similar to that of the conventional model, even for the number of neurons not used for automatic adjustment of parameters.
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Aihara, K., Takabe, T., Toyoda, M.: Chaotic neural networks. Phys. Lett. A 144(6 & 7), 333–340 (1990)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79, 2554–2558 (1982)
Osana, Y., Hagiwara, M.: Separation of superimposed pattern and many-to-many associations by chaotic neural networks. In: Proceedings of IEEE and INNS International Joint Conference on Neural Networks, Anchorage, vol. 1, pp. 514–519 (1998)
Osana, Y.: Recall and separation ability of chaotic associative memory with variable scaling factor. In: Proceedings of IEEE and INNS International Joint Conference on Neural Networks, Hawaii (2002)
Okada, T., Osana, Y.: Chaotic associative memory with adaptive scaling factor. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10614, pp. 713–721. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68612-7_81
Nakada, M., Osana, Y.: Chaotic complex-valued associative memory. In: Proceedings of International Symposium on Nonlinear Theory and its Applications, Vancouver, pp. 16–19 (2007)
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Karakama, D., Katamura, N., Nakano, C., Osana, Y. (2018). Chaotic Complex-Valued Associative Memory with Adaptive Scaling Factor. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_50
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DOI: https://doi.org/10.1007/978-3-030-01421-6_50
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