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- W4308532833 abstract "Recent advances have demonstrated that machine learning models are effective methods for predicting chaotic systems. Although short-term chaos prediction can be successfully realized by seemingly different machine learning models, an intriguing question of their correlation is still unknown. Here, we focus on three commonly used machine learning models that are reservoir computing, long-short term memory networks, and deep belief networks, respectively. We find that these selected models present almost identical long-term statistical properties as that of a learned chaotic system. Specifically, we show that these machine learning models have the same correlation dimension and recurrence time. Furthermore, by sharing a common signal, we realize synchronization, cascading synchronization, and coupled synchronization among machine learning models. Our findings reveal the equivalence of machine learning models in characterizing and modeling chaotic systems. • We investigate what are the common features among distinct machine learning models. • We show that long-term behaviors of machine learning models are almost the same. • We realize several types of synchronization among distinct machine learning models. • Our findings reveal their equivalence in characterizing and modeling chaotic systems." @default.
- W4308532833 created "2022-11-12" @default.
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- W4308532833 date "2022-12-01" @default.
- W4308532833 modified "2023-09-27" @default.
- W4308532833 title "Equivalence of machine learning models in modeling chaos" @default.
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- W4308532833 doi "https://doi.org/10.1016/j.chaos.2022.112831" @default.
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