低噪声水平混沌时序的预测技术及其应用研究

研究含有噪声的混沌时序的除噪及其重构技术，基于除噪混沌数据的预测技术及其应用．应用混沌时序的奇异值分解技术对混沌时序的噪声进行了剥离，将混沌时序的相空间分解成为值域空间和虚拟的噪声空间，在值域空间内重构了原混沌时序，并在此基础上，确立了非线性模型的阶，利用所提出的非线性模型对时序进行了预测研究工作，研究结果表明，该非线性模型具有很强的函数逼近能力，所采用的混沌预测方法对相应的实际问题有着一定的指导意义．

Prediction Techniques of Chaotic Time Series and Its Applications at Low Noise Level

Not only the noise reduction methods of chaotic time series with noise and its reconstruction techniques were studied, but also prediction techniques of chaotic time series and its applications were discussed based on chaotic data noise reduction. first the phase space of chaotic time series was decomposed to range space and null noise space. Secondly original chaotic time series was reconstrucled in range space. Lastly on the basis of the above, the order of the nonlinear model was established and the nonlinear model was made use of to predict some research. The result indicates that the nonlinear model has very strong ability of approximation function, and Chaos prediction method has certain tutorial significance to the practical problems.