基于网络模体与簇结构的非线性时间序列预测方法研究

项目名称： 基于网络模体与簇结构的非线性时间序列预测方法研究

项目编号： No.61201428

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 电子学与信息系统

项目作者： 孟庆芳

作者单位： 济南大学

项目金额： 27万元

中文摘要： 近年来把时间序列构造为复杂网络的方法在非线性时间序列分析与复杂网络之间架起了桥梁，基于复杂网络的非线性时间序列分析方法成为一个重要的研究热点。但是基于复杂网络的非线性时间序列预测方法的研究国内外未见有报道。基于过嵌入法，提出适用于非平稳时间序列的复杂网络构造法；针对非线性模型泛化推广能力差和存在过拟合等问题，利用构造的复杂网络的空间信息与分层结构，重点提出基于复杂网络模体和网络簇结构的非线性时间序列预测方法，提高模型预测精度并降低模型复杂度；基于复杂网络统计量与模型预测误差，提出脑电信号特征提取方法，并应用于分析不同模式脑电信号（癫痫脑电、发作间歇期脑电、发作前期脑电）的动力学状态，提高癫痫检测和癫痫发作预测的精度；构建基于复杂网络的非线性时间序列预测方法及其应用平台。本项目研究成果在基于复杂网络的非线性时间序列分析方法研究与脑电信号非线性动力学分析方面都具有重要意义。

中文关键词： 非线性信号处理理论与模型；非线性时间序列分析；复杂网络；局域预测方法；脑电信号

英文摘要： In recent years with the method to transform a time series into a complex network, a natural bridge between complex networks theory and nonlinear time series analysis has now been built. The nonlinear time series analysis method based on complex network has become an important research focus. However, The research on the nonlinear time series prediction method based on complex network has not been reported at home and abroad. Based on the overembedding method, the method to transform a time series into a complex network for nonstationary time series will be proposed. To improve the nonlinear models' generalization capability and to solve the nonlinear models' overfitting problem, based on the spatial information and the hierarchical structure of the constructed complex network from a time series, we will propose the nonlinear time series prediction methods based on the complex network motif and based on the complex network cluster structure, which improve the nonlinear models' prediction accuracy and reduce the nonlinear models' complexity. Based on the complex network statistical measures and based on the nonlinear models' prediction error, the feature extraction methods for EEG signals will be proposed and applied to analyze various EEG signals' pattern (epileptic ictal EEG, interictal EEG, preictal EEG), whic

英文关键词： Theory and model of the nonlinear signal processin；Nonlinear time series analysis；Complex network；Local prediction method；EEG signals