基于模糊信息粒化与混合神经网络的混沌时间序列预测

为了提高对混沌时间序列预测的精准度，提出了一种基于模糊信息粒化和注意力机制的混合神经网络预测模型。首先对数据进行归一化处理，利用模糊信息粒化对数据的复杂度进行简化；然后将经过相空间重构后的样本输入卷积神经网络(CNN)提取空间特征；再利用长短期记忆神经网络(LSTM)进一步提取时间特征；最后将融合特征传递给注意力机制提取关键特征，得出预测结果。选取Logistic、洛伦兹和太阳黑子混沌时间序列进行实验，并与CNN-LSTM-Att模型、CNN-LSTM模型、FIG-CNN模型、FIG-LSTM模型、CNN模型、LSTM模型、支持向量机(SVM)及误差逆传播(BP)模型进行对比分析。结果表明，所提的预测模型预测精度更高，误差更小。

Chaotic Time Series Prediction Based on Fuzzy Information Granulation and Hybrid Neural Network

To improve the prediction accuracy of chaotic time series, we propose a hybrid neural network prediction model based on fuzzy information granulation and an attention mechanism. First, we normalize the data, simplify the complexity of the data using fuzzy informational granulation (FIG), and then add the samples reconstructed from phase space into a convolution neural network (CNN) to extract spatial features. Next, we use the long short-term memory network (LSTM) to further extract the temporal features. Finally, we transmit the fusion features to the attention mechanism to extract the key features and obtain the prediction results. We select Logistic, Lorenz, and sunspot chaotic time series for experiments and compare the model with the CNN-LSTM-Att, CNN-LSTM, FIG-CNN, FIG-LSTM, CNN, and LSTM models, and support vector machine (SVM) and error back propagation (BP) neural network model. The results show that the proposed prediction model has a higher prediction accuracy and a smaller error value.