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- W4229009289 abstract "Silicon-based optical chaos has many advantages, such as compatibility with complementary metal oxide semiconductor (CMOS) integration processes, ultra-small size, and high bandwidth. Generally, it is challenging to reconstruct chaos accurately because of its initial sensitivity and high complexity. Here, a stacked convolutional neural network (CNN)-long short-term memory (LSTM) neural network model is proposed to reconstruct optical chaos with high accuracy. Our network model combines the advantages of both CNN and LSTM modules. Further, a theoretical model of integrated silicon photonics micro-cavity is introduced to generate chaotic time series for use in chaotic reconstruction experiments. Accordingly, we reconstructed the one-dimensional, two-dimensional, and three-dimensional chaos. The experimental results show that our model outperforms the LSTM, gated recurrent unit (GRU), and CNN models in terms of MSE, MAE, and R-squared metrics. For example, the proposed model has the best value of this metric, with a maximum improvement of 83.29% and 49.66%. Furthermore, 1D, 2D, and 3D chaos were all significantly improved with the reconstruction tasks." @default.
- W4229009289 created "2022-05-08" @default.
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- W4229009289 date "2022-05-01" @default.
- W4229009289 modified "2023-10-17" @default.
- W4229009289 title "High precision reconstruction of silicon photonics chaos with stacked CNN-LSTM neural networks" @default.
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- W4229009289 doi "https://doi.org/10.1063/5.0082993" @default.
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- W4229009289 hasPublicationYear "2022" @default.
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