王红瑞, 宋宇, 刘昌明, 陈家军. 混沌理论及在水科学中的应用与存在的问题[J]. 水科学进展, 2004, 15(3): 400-407.
引用本文: 王红瑞, 宋宇, 刘昌明, 陈家军. 混沌理论及在水科学中的应用与存在的问题[J]. 水科学进展, 2004, 15(3): 400-407.
WANG Hong-rui, SONG Yu, LIU Chang-ming, CHEN Jia-jun. Application and issues of chaos theory in hydroscience[J]. Advances in Water Science, 2004, 15(3): 400-407.
Citation: WANG Hong-rui, SONG Yu, LIU Chang-ming, CHEN Jia-jun. Application and issues of chaos theory in hydroscience[J]. Advances in Water Science, 2004, 15(3): 400-407.

混沌理论及在水科学中的应用与存在的问题

Application and issues of chaos theory in hydroscience

  • 摘要: 在简要介绍表征系统混沌特征常用方法的基础上,认为混沌理论在水科学中的应用主要集中在水文时间序列性质的判定和非线性预测模型研究上,并就这两方面的国内外研究进展进行了综述。通过对其应用中存在的主要问题:延迟时间的确定、数据量大小、噪声等进行分析和讨论,认为水科学问题中混沌的假设是合理的。但对于任何有限长度和受噪声干扰的时间序列来说,严格判定其相空间是确定性系统还是随机系统几乎是不可能的,纯粹的混沌和纯粹的随机都只是数学上的一种理想状态。因此,对于所研究的水科学问题是否存在混沌现象,仅采用单一的方法所取得的结论,不能作为判定依据,而仅表明可能存在混沌特征。所以有必要采用多种研究方法,综合刻划某一水科学问题中的混沌迹象,判定该序列是以混沌成分为主还是以随机性成分为主。提出分形上的混沌动力系统以及混沌与神经网络相结合的方法,可为水科学问题的研究能提供一些新的思路。

     

    Abstract: The common methods characterizing chaotic system are briefly introduced in this paper.The most domestic and overseas advance in this filed are summarized,it is concluded that the main applications of chaos theory in hydroscience focused on hydrologic time series analysis and nonlinear prediction model.The most problems revealed in the past studies,such as delay time,data size,noise,are analyzed and discussed in this paper,so the hypothesis of chaotic character of hydroscience problems is reasonable.But as for any finite and noisy time series,it is almost impossible to determine strictly whether the phase-space is a determinate or stochastic system because whether pure stochastic or chaos is an ideal state of mathematics. Any observation achieved through single method may not be used as evidence of chaos in hydroscience problems,can only be regarded as chaotic character.Various ways should be employed to characterize chaos of hydroscience and define whichone, chaos or stochastic is dominating.Finally,we suggest that the fractal chaos dynamical system and the mean of combining chaos with neural net provide several new methods in the future studying.

     

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