一类对称的Kolmogorov系统的定性研究（Ⅰ）

本文对一类对称的三次微分系统进行探讨，对每个奇点导出表示其鞍结性的型号公式。给出（Ｋ３）所有的六种全局相图。     

熵(Ⅱ)

介绍了解决遍历理论经典问题的Kolmogorov熵,以及为研究拓扑动力系统而产生的拓扑熵等概念,进而引导读者对这一引人入胜的领域去进行研究.     

Research on Chaotic Time Series Prediction Based on K-entropy and RBF Neural Networks

In this paper, a method of direct multi-step prediction of chaotic time series is proposed, which is based on Kolmogorov entropy and radial basis functions neural networks. This is done first by reconstructing a phase space using chaotic time series, then using K-entropy as a quantitative parameter to obtain the maximum predictability time of chaotic time series, finally the predicted chaotic time series data can be acquired by using RBFNN. The application considered is Lorenz system. Simulation results for direct multi-step prediction method are compared with recurrence multi-step prediction method. The results indicate that the direct multi-step prediction is more accurate and rapid than the recurrence multi-step prediction within the maximum predictability time of chaotic time series. So, it is convenient to forecast and control with real time using the method of direct multi-step prediction.     

一种裂隙岩体力学参数的评价方法及应用

针对裂隙岩体的力学参数选取问题，提出了一种两阶段评价方法.通过裂隙岩体力学参数的三维空间分布情况，评价其各向同性与各向异性状态，对两类情况采用不同的计算模型处理；采用Kolmogorov-Smirnov(K-S)检验法对力学参数的概率分布模型进行假设检验，对检验合格的试验数据分布进行置信区间选取，在给定的置信区间内获取计算参数.在某地下硐室工程中运用该方法进行了开挖模拟计算，计算位移与实测位移吻合较好.     

金沙江流域月径流时间序列的混沌分析

在介绍重构相空间技术的主要定量指标(关联维数D2和柯尔莫奇诺夫熵)的基础上,以长江上游金沙江流域小黄瓜园站和蔡家村站的月径流时间序列为例详细说明了求取时间序列中的混沌特征数的方法;并且采用主分量分析(PCA分布)方法进一步验证了两个站的径流序列具有混沌特性.得到金沙江流域径流序列的预测年限不应超过7～9个月,为金沙江流域径流预测提供了科学的依据.     

基于PSRT的大连豆粕期货价格的混沌判据

通过相空间重构技术,对大连豆粕期货价格增长率的时间序列分别进行相空间重构,应用Wolf方法得出最大的Lyapunov指数,从而给出系统混沌存在的证据;利用关联函数求出关联维度和Kolmogorov熵,从而给出对系统的混沌程度的估计和对大连豆粕期货价格进行有效性预测的时间尺度.     

A NOTE ON THE PAPER “UNIFORM PERSISTENCE IN DISCRETE SEMIDYNAMICAL SYSTEM AND ITS APPLICATION”

1.IntroductionAsweknow,theresearchonthepersistenceofnonautonomousKolmogorovspeciessystems:isallalongaveryimportantsllbjectinthetheoryofmathematicalbiology.But,sofar,theworkonitisnotenough.ThemainresultsachievedarealmostconcernedwiththeLotka-Volterrasystems.Recently,in[l]theauthorshavehadahelpfultryonthestudyofuniformpersistenceoftwodimensionalperiodicKolmogorovsystem:Unfortunately,thereisamistakeintheproofofthemainTheorem3.3in[1].Themistakemakestheconclusionofthistheoremdoubtful.Asanimprov…     

算法复杂性研究历史述评

主要讨论了算法复杂性的产生、内容及其意义。     

Weights and robustness of model-based seasonal adjustment

Model-based seasonal adjustment implicitly defines a set of weights at the ends of series as well as in the middle. Until now, with the exception of very simple models, the weights have been obtained numerically. In this paper we give the analytical expressions for the weights for both the structural and the ARIMA framework for a model which contains trend, seasonal and irregular component. In the final part of the paper we address the question of robustness of model-based seasonal adjustment. We analyse practically, using real time series, and theoretically, through the analysis of the shape of the weights, how the fitting of different specifications for the non-seasonal part affects the extraction of the seasonal component. © 1998 John Wiley & Sons, Ltd.     

三次 Kolmogorov 系统的二次代数曲线轨线的判定及分类

研究了三次Kolmogorov模型的二次代数曲线解的判定及分类.利用具体的非奇异线性变换,将二次曲线化简,同时将方程作了相应的变换,新方程的二次曲线轨线等价于原方程的二次曲线轨线.由于计算量比较大,采用Mathematica计算机符号运算系统使问题得到比较满意的结果.