College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
United Microelectronics Center Co., Ltd, Chongqing 401332, China.
Chaos. 2022 May;32(5):053112. doi: 10.1063/5.0082993.
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.
基于硅的光学混沌具有许多优点，例如与互补金属氧化物半导体 (CMOS) 集成工艺兼容、超小尺寸和高带宽。由于其初始灵敏度和高复杂性，通常很难准确地重建混沌。在这里，提出了一种堆叠卷积神经网络 (CNN)-长短时记忆 (LSTM) 神经网络模型，以高精度重建光学混沌。我们的网络模型结合了 CNN 和 LSTM 模块的优势。此外，还引入了集成硅光子微腔的理论模型，以生成用于混沌重建实验的混沌时间序列。因此，我们重建了一维、二维和三维混沌。实验结果表明，在均方误差 (MSE)、平均绝对误差 (MAE) 和 R 方度量方面，我们的模型优于 LSTM、门控循环单元 (GRU) 和 CNN 模型。例如，所提出的模型具有该指标的最佳值，最大改进率为 83.29%和 49.66%。此外，重建任务显著提高了 1D、2D 和 3D 混沌。
