CAJ | 학술논문

考虑单变量在混沌时间序列预测中的不足,文章利用多变量模型进行混沌时间序列的预测.针对多变量预测过程中的维数过高问题,文章结合主元分析理论(PCA)和回声状态网络(ESN),构建了基于PCA和ESN的多变量混沌时间序列预测模型,将PCA降维后的时间序列数据输入ESN网络进行预测分析.论文对由Lorenz动态方程生成的三变量混沌时间序列进行了仿真实验,结果表明该模型有效地提高了预测的精度和预测的效率,是一种有效的混沌时间序列预测方法.
고필단변량재혼돈시간서렬예측중적불족,문장이용다변량모형진행혼돈시간서렬적예측.침대다변량예측과정중적유수과고문제,문장결합주원분석이론(PCA)화회성상태망락(ESN),구건료기우PCA화ESN적다변량혼돈시간서렬예측모형,장PCA강유후적시간서렬수거수입ESN망락진행예측분석.논문대유Lorenz동태방정생성적삼변량혼돈시간서렬진행료방진실험,결과표명해모형유효지제고료예측적정도화예측적효솔,시일충유효적혼돈시간서렬예측방법.
Considering the shortages in the prediction of chaotic time-series using single variable, this paper studies a new multivariate chaotic time-series prediction model, which is based on the principal components analysis (PCA) and echo state networks (ESN). This new prediction model extracts the joint information of multiple variables by PCA method firstly, then uses the ESN to make predictions. Finally,the simulation example, which is from the typical Lorenz equation, shows that the new model will obtain the well performance both in the precision and speed.