混沌时间序列的长期预测方法研究-信号与信息处理专业论文.docx

混沌时间序列的长期预测方法研究下，多重分支时间延迟神经网络能够保持稳定的条件，即能够依靠网络内部的结构因素 混沌时间序列的长期预测方法研究 下，多重分支时间延迟神经网络能够保持稳定的条件，即能够依靠网络内部的结构因素 使受扰输出限制在平衡状态的一个有限邻域内时，网络结构应该满足的条件。 (4)结合主成分分析和神经网络进行多元变量时间序列预测方法的研究。复杂系统 的动态特性通常包含在多个变量时间序列的演化轨迹中，需要研究多元变量相空间重构 方法。本文沿用单变量混沌时间序列相空间重构预测的思路，扩展主成分分析方法应用 于提取多元变量输入样本中的主成分，以降低输入变量维数，重构多元变量相空间：利 用时间延迟前馈神经网络实现多输入与多输出之间的函数映射。仿真结果说明该方法可 以进行多元变量时间序列的建模研究，实现较高精度的预测。 关键词：混沌序列：预测；卡尔曼滤波；多重分支神经网络；多变量时间序列 ．II． 塑三燮主堂垡笙塞 塑三燮主堂垡笙塞 一 Study 011 Long-term Prediction Technology of Chaofic Time Series This paper discusses the mechanism ofmodeling and prediction ofchaotic time series．()n the base of analyzing chaotic characteristics，the aim is constructing a methodology to describe univariate or multivariate series of complex natural system．Within the range of predictability， long term prediction is made by two ways．One is tracking the s扛ange attractor’S trajectory in real time and the other is tracking the multiple neighboring trajectories at the salne time． Corresponding theory iS also anal弦ed．The main research contents and research conclusions are lisled as follows． (1)Method study on chaotic noise reduction combining with the atlractor trajectories features in phase sDace．The chaotic time sel．ies behave unknown chaotic character and have a big data set，which requires the improvement of the traditional local average nonlinear noise reduction method．A nonlinear local approximation noise reduction method is presented．This method USeS the simple∞。norln to compute the distance between two points and builds the weighted model in the neighborhood，which call better correct the position of data points in phase space to approximate the real chaotic attractor trajectories more closely．Simulation results show that the improved method Call effectively reduce the noise，keep the chaotic characteristics ofthe nonlinear system and better distinguish the closed trajectories in the phase space· (2)Self-adaptive prediction study using reconstructing system equations．A new concept named Generalized