Abstract
In this paper, we propose a chaotic complex-valued associative memory with adaptive scaling factor independent from multi-values. This model is based on the conventional chaotic complex-valued associative memory with adaptive scaling factor that can realize dynamic associations of multi-valued patterns. In the conventional chaotic complex-valued associative memory with adaptive scaling factor, parameters of the chaotic complex-valued neuron model are automatically adjusted according to the internal state of neurons. In the conventional model, a multi-value pattern is expressed by assigning points at positions obtained by equally dividing a unit circle of the complex plane into S multiple values. It has been confirmed that almost same recall ability can be obtained as in the case of performing manual adjustment in the model for \(S=4,\ 6,\ 8\), but no study has been conducted for other cases. In addition, it is known that the optimum method of automatically adjusting parameters also differs depending on the value of S. In this study, we also conduct experiments at \( S = 10,\ 12,\ 14\) and 16, and propose a method to automatically adjust the parameters of the chaotic complex-valued neuron model independently from the value of S. Computer experiments were carried out and it was confirmed that automatic adjustment of parameters can be performed in the proposed model without depending on multi-values.
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References
Jankowski, S., Lozowski, A., Zurada, J.M.: Complex-valued multistate neural associative memory. IEEE Trans. Neural Netw. 7(6), 1491–1496 (1996)
Aihara, K., Takabe, T., Toyoda, M.: Chaotic neural networks. Phys. Lett. A 144(6–7), 333–340 (1990)
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)
Nakada, M., Osana,Y.: Chaotic complex-valued associative memory. In: Proceedings of International Symposium on Nonlinear Theory and its Applications, Vancouver (2007)
Karakama, D., Katamura, N., Nakano, C., Osana, Y.: Chaotic complex-valued associative memory with adaptive scaling factor. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11140, pp. 523–531. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01421-6_50
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. In: Proceedings of the National Academy of Sciences of the USA, vol. 79, pp. 2554–2558 (1982)
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Goto, H., Osana, Y. (2019). Chaotic Complex-Valued Associative Memory with Adaptive Scaling Factor Independent of Multi-values. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science(), vol 11727. Springer, Cham. https://doi.org/10.1007/978-3-030-30487-4_6
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DOI: https://doi.org/10.1007/978-3-030-30487-4_6
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