摘要
混沌时间序列预测是混沌理论的一个重要应用领域和研究热点,目前它在信号处理、自动化控制等领域中已得到了广泛的应用。本文联系支持向量机(SVM)和混沌时间序列预测的相关理论,建立基于二者的变形序列预测模型。同时,结合具体实例从变形时间序列的混沌识别、相空间重构以及预测模型的参数优化等方面探讨了模型的具体建立过程。实验结果表明,该模型的预测精度要优于BP神经网络。
Prediction for chaotic time series is an important application field and hot research spot of chaos theory.At present,it has been widely used in the field of signal processing,automatic control and so on.This paper combined with the related theory of support vector machine（SVM） and chaos time series forecasting to establish a deformation sequence prediction model.At the same time,the paper discussed the concrete procedures for the prediction model by deformation time sequence of chaos identification,phase space reconstruction and parameter optimization with real examples.The experimental results show that this model is superior to the prediction precision of BP neural network.
出处
《工程勘察》
2013年第9期65-68,共4页
Geotechnical Investigation & Surveying
