论文标题
使用深度学习中的混沌吸引子预测式二象强制性逻辑图
Prediction of chaotic attractors in quasiperiodically forced logistic map using deep learning
论文作者
论文摘要
我们预测了使用众所周知的深度学习框架长的短期记忆，对准二象强制性图表进行了两种不同的混乱动力学。我们生成两个数据集，并在培训过程中使用一个，而在测试过程中使用另一个。使用称为根平方误差的度量公制评估预测值，并使用散点图进行可视化。使用模型层中的单元数量评估长短期内存模型的鲁棒性。我们还对所考虑的系统进行多步骤预测。我们表明，被认为是长期的短期记忆模型在预测混沌吸引子最多三个步骤方面表现良好。
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.
