Abstract
This paper proposes a Machine Learning (ML) algorithm that exhibits markedly different behaviour when trained with chaotic input data, compared to non-chaotic data. It will be demonstrated that the output of the ML system is itself chaotic when it is trained on a chaotic input. The proposed algorithm is a deep network with both direct and recurrent connections between layers, as well as recurrent connections within each layer.
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Berglund, E. (2019). Modelling Chaotic Time Series Using Recursive Deep Self-organising Neural Networks. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science(), vol 11943. Springer, Cham. https://doi.org/10.1007/978-3-030-37599-7_55
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DOI: https://doi.org/10.1007/978-3-030-37599-7_55
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