基于小波回声状态网络的混沌时间序列预测

混沌现象普遍存在于自然界及人类社会中,因此混沌时间序列预测具有重要意义. 提出了一种新的混沌时间序列预测模型------小波回声状态网络,该模型可以有效克服传统回声状态 网络模型中普遍存在的病态矩阵问题,提高了混沌时间序列预测精度.通过对Lorenz、含噪声Lorenz 及间歇式反应釜釜温三个时间序列的预测,将小波回声状态网络与传统回声状态网络进行了比较. 结果表明,小波回声状态网络与传统回声状态网络相比,预测精度提高一倍以上且预测结果更加稳定.

Chaotic time series prediction based on wavelet echo state network

Chaos is widespread in nature and human society, so the prediction of chaotic time series is very important. In this paper, we propose a new chaotic time series prediction model--- echo state network based on wavelet, which can effectively overcome the ill-posed problem that exists in traditional echo state networks. And it also has a good generalization ability. Three time series are used to test the new model, i.e., Lorenz time series, Lorenz time series with added noise and batch reactor vessel temperature time series. Results suggest that the new proposed method can achieve a higher predictable accuracy, better generalization and more stable prediction results than traditional echo state networks.