南华大学学报：自然科学版2012，Vol.26Issue(2)：26-31,6.
基于BP神经网络的混沌时间序列预测方法及应用研究
The Research of Prediction Methods and Application of Chaotic Time Series Based on BPNN
摘要
Abstract
Neural network is widely used in time series forecasting, but still not a clear analytical expression for how to choose the number of the input layer neurons of the neural net- work model. To solve this problem, the paper reconstructs phase space, and determines the series is chaotic time series or not by Lyapunor index in the nonlinear dynamical systems. The correlation dimension of chaotic attractor is calculated through the G-P algorithm if the series is chaotic time series. Then embedding dimension of phase-space is obtained as the number of neurons in neural networks. Testing aluminum price data proves above-men- tioned method has good accuracy for short-term forecasting of time series. Then the paper forecasts aluminum price in the future by the method.关键词
混沌时间序列/最大Lyapunov指数/G-P算法/BP神经网络Key words
chaotic time series/lyapunor index/G-P algorithm/BPNN分类
计算机与自动化引用本文复制引用
霍晓宇,杨仕教,吴长振..基于BP神经网络的混沌时间序列预测方法及应用研究[J].南华大学学报：自然科学版,2012,26(2):26-31,6.基金项目
国家自然科学基金资助项目 （）
