基于深度学习的拟周期强迫Logistic映射混沌吸引子预测
Prediction of chaotic attractors in quasiperiodically forced logistic map using deep learning
摘要：
我们使用著名的深度学习框架长短期记忆预测了准周期强迫Logistic映射的两种不同的混沌动力学。我们生成两个数据集，一个用于训练过程，另一个用于测试过程。预测值使用称为均方根误差的度量进行评估，并使用散点图进行可视化。长短期记忆模型的鲁棒性使用模型层中的单元数进行评估。我们还考虑系统的多步预测。我们发现，所考虑的长短期记忆模型表现良好，在预测混沌吸引子高达三个步骤。
英文摘要：
We forecast two different chaotic dynamics of the quasiperiodically forced logistic map using the well-known deep learning framework Long Short-Term Memory. We generate two data sets and use one in the training process and the other in the testing process. The predicted values are evaluated using the metric called Root Mean Square Error and visualized using the scatter plots. The robustness of the Long Short-Term Memory model is evaluated using the number of units in the layers of the model. We also make multi-step forecasting of the considered system. We show that the considered Long Short-Term Memory model performs well in predicting chaotic attractors upto three steps.
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