At first , construct ten sets of surrogate data for each emg data , then calculate the correlation dimension ( dcorr ) , correlation time , maximum lyapunov exponent ( a , ) , l - z complexity and approximate entropy ( apen ) of both the original emg data and surrogate data , and then compare them 构造这四例肌电信号的代替数据(各十组) ，分别计算原数据及其代替数据的关联维、关联时间、最大李雅普诺夫指数、 l - z复杂度和近似熵，我们发现，四例原数据的以上所有特征量都与各自所对应的十组代替数据存在明显差别。
This paper uses systematic project for energy system according to jiangsu and ten province energy conditions in west . it applies nonlinear chaotic dynamics theory in energy system and builds the chaotic dynamics model of energy system : chaotic time series model . the largest lyapunov index energy time series has been calculated according to decimal quantity method 本文根据江苏及西部十省能源状况，利用系统工程的方法对能源系统进行研究，将非线性混沌动力学理论应用于能源系统中，建立能源系统的混沌动力学模型：混沌时间序列模型，采用小数据量方法来计算了能源时间序列的最大李雅普诺夫指数。
Then r / s analysis , phase space reconstruction of the system , chaos analysis and fractals analysis are done through matlab program , based on original data of hushen stock markets compositive index from year 1991 to year 2002 . and the author draws a conclusion based on original data that china ' s stock market obeys low - dimension fractals and ebb - chaos in terms of the experimentation result : hurst exponents are between 0 and 1 , memory cycles are obvious , lyapunov exponents are more than zero and chaotic attractors correlative dimensions are between 2 and 3 in hushen stock markets in this thesis the concept information noises is put forward . stock market information about policy and company of the last ten years is packed up and classified for regulators make decisions in terms of power the factor influences the stock market index 之后文章以中国股市1991年至2002年上海和深圳综合指数每日收盘价原始数据为研究对象，在matlab程序实验条件下，进行了两地股市系统的r / s分析、系统相空间重构、混沌分析、分形分析；获取了两地股市系统的赫斯特指数(沪深股市赫斯特指数均大于0 . 5而小于1 ) 、非周期记忆循环周期（沪深股市都有明显的记忆循环周期） 、最大李雅普诺夫指数（两市都大于0 ）和吸引子的关联维数（两市都在2到3维之间） ；从而得出中国股市系统是低维分形的、弱混沌的（基于原始数据）结论。
