Abstract
This study shows how sensory-action sequences of imitating finite state machines (FSMs) can be learned by utilizing the deterministic dynamics of recurrent neural networks (RNNs). Our experiments indicated that each possible combinatorial sequence can be recalled by specifying its respective initial state value and also that fractal structures appear in this initial state mapping after the learning converges. We also observed that the RNN dynamics evolves, going back and forth between regions of window and chaos in the learning process, and that the evolved dynamical structure turns out to be structurally stable after adding a negligible amount of noise.
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© 2003 Springer-Verlag Berlin Heidelberg
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Nishimoto, R., Tani, J. (2003). Learning to Generate Combinatorial Action Sequences Utilizing the Initial Sensitivity of Deterministic Dynamical Systems. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_54
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DOI: https://doi.org/10.1007/3-540-44868-3_54
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