论文标题
基于深层LSTM的混合模型，用于预测高维混沌系统
A hybrid model based on deep LSTM for predicting high-dimensional chaotic systems
论文作者
论文摘要
我们提出了一种混合方法，将深层短期记忆（LSTM）模型与动态系统的不精确经验模型相结合，以预测高维混沌系统。深层层次结构通过叠加多个复发性神经网络层来编码在LSTM中，并使用ADAM优化算法训练混合模型。 Mackey-Glass系统和Kuramoto-Sivashinsky系统的统计结果是根据均方根误差（RMSE）的标准（RMSE）和异常相关系数（ACC）获得的，分别使用Singe-Layer LSTM，多层LSTM和相应的混合方法。数值结果表明，提出的方法可以在重建混乱的吸引子时有效避免多层LSTM模型的快速差异，并证明了基于梯度下降方法和经验模型的深度学习组合的可行性。
We propose a hybrid method combining the deep long short-term memory (LSTM) model with the inexact empirical model of dynamical systems to predict high-dimensional chaotic systems. The deep hierarchy is encoded into the LSTM by superimposing multiple recurrent neural network layers and the hybrid model is trained with the Adam optimization algorithm. The statistical results of the Mackey-Glass system and the Kuramoto-Sivashinsky system are obtained under the criteria of root mean square error (RMSE) and anomaly correlation coefficient (ACC) using the singe-layer LSTM, the multi-layer LSTM, and the corresponding hybrid method, respectively. The numerical results show that the proposed method can effectively avoid the rapid divergence of the multi-layer LSTM model when reconstructing chaotic attractors, and demonstrate the feasibility of the combination of deep learning based on the gradient descent method and the empirical model.
