提出一种基于混沌预测的OGY方法。
据此，建立了岩体工程位移观测数据的混沌预测方法。
Thus, the chaotic analytic method is set up for the prediction of the measured displacement time series of rock mass engineering.
基于混沌预测误差的目标检测算法是检测混沌背景下目标的算法。
The detection algorithm based on chaotic prediction error is used to detect objects under a chaotic background.
电力负荷的混沌预测是建立在重构电力负荷序列的相空间基础之上的。
The chaotic forecasting of power load is based on reconstructing phase space of the power load series.
将混沌预测值与采用灰色GM（1，1）模型所得到的预测值进行了对比分析。
The effectiveness of the proposed model is tested by comparison of the monitoring data with GM(1,1) model.
应用混沌预测方法，对气液两相单孔鼓泡系统的压力波动时间序列进行了短期预测。
A short-term prediction method developed on the basis of the deterministic chaos theory is used to predict the time series of pressure fluctuations in a gas-liquid bubble column with a single orifice.
第二部分基于水文序列变化的混沌特性，对水文时间序列的混沌预测模型进行了研究。
In the second part, the chaotic prediction models for hydrological time series are studied in terms of the chaotic characteristics of hydrological evolution process.
传统的混沌水印置乱方案存在的两类不足：抗混沌预测技术的能力和有限字长效应的影响。
The traditional chaotic watermark scrambling method has two weak points: the ability of anti-forecast technology and the influence of word length.
在混沌相空间重构局域法的基础上提出了几种新的预测方法，并将气象因子引入到了混沌预测中。
Then new load forecasting methods are developed based on the chaotic phase space reconstruction, in which climate factors are considered.
根据单变量时间序列的混沌预测方法，只要嵌入维数和延迟时间选择得合理，便能进行精确的预测。
Based on the prediction method of univariate time series, and according to the proper selection of dimension and delay time, the time series can be predicted precisely.
针对这个问题，对基于混沌预测误差的目标检测结果进行了修正，修正后的结果更接近于目标的真实大小。
Aiming at the problem, the detection result based on chaotic prediction error is modified. The modified result is much closer to the real size of a target.
本文采用混沌局域预测法建立了一个线性模型对浸润面实测数据进行预测，预测结果表明，建立的混沌预测模型计算量较小，易于操作且预测精度较高。
The results show that the calculating quantity of the proposed forecasting model is less and the model is prone to be operated and the precision of forecasting is better.
上世纪90年代，许多经济学家开始将混沌理论视为一种提供预测模型的方法。
In the 1990s, many economists began to look at chaos theory as a way of providing models for forecasting.
这些系统正好处于稳定、有序行为和不可预测的混沌世界（如乱流）之间的边界上。
These systems are right on the boundary between stable, orderly behavior and the unpredictable world of chaos, as exemplified by turbulence.
这些系统就是刚刚好在稳定有序行为的边界-就像一个摆动的钟摆-还有难以预测的混沌世界，如湍流一般。
These systems are right on the boundary between stable, orderly behaviour - such as a swinging pendulum - and the unpredictable world of chaos, as exemplified by turbulence.
尽管混沌表明动力学系统的障碍，但是定位混沌起源和观察它的发生可能允许研究人员去预测，也许甚至抵消某些结果。
Although chaos marks the disorder of a dynamical system, locating the origin of chaos and watching it develop might allow researchers to predict, and maybe even counteract, certain outcomes.
本文介绍了电子对抗中对混沌时间序列进行预测的一种数学模型和一种基于混沌序列的反侦察系统。
In this paper, the prediction of mathematical model for chaotic sequence in electronic antagonism and the anti-reconnaissance based on chaotic sequence are presented.
基于对混沌信号分析方法的研究，利用混沌序列固有的确定性，我们提出了一种预测干扰的方法。
Based on the analysis and the deterministic characterization of chaotic signals, we propose a interference method by constructing a nonlinear predictive model.
通过对实测交通流进行混沌判别，可以为实际交通流的预测和控制提供理论指导。
The identification of chaos in the real traffic flow can provide theoretical guidance for forecasting and control of real traffic flow.
混沌对初始条件的敏感性决定了在其可预测范围内，实现长期预测十分困难。
Owing to the strong sensitivity of chaotic system to initial conditions, it is highly difficult to make long term prediction.
重构合适的相空间，使空间轨道充分展开是进一步混沌序列预测的前提。
Reconstructing proper ph ase space to spread space orbit completely is the precondition of further chaotic series forecasting.
识别混沌是对非线性时间序列进行分析、预测、控制的基础。
How to identify chaos is the foundation of analysis, prediction and control of nonlinear time series.
基于混沌时间序列的局域线性预测模型，提出了多嵌入维的短期负荷预测方法。
Based on local linear prediction model of chaotic time series, short-term load forecasting method on multi-embedding dimension is presented.
采用新型多重分支时间延迟神经网络进行混沌时间序列预测研究。
A new multi-branch time delay neural network is adopted to conduct prediction research on chaotic time series.
给出了基于径向基函数网络的混沌时间序列预测的方法。
A method based on radial basis function networks for forecasting chaotic time series is proposed.
它利用了混沌对初始条件敏感、轨迹具有不可预测性的特点来产生跳频序列。
It utilizes the characteristic of sensitivity to initialization and indeterminism to contrail, so chaotic property determines the performance of chaotic sequence.
本文利用模糊神经网络的通用逼近性，证明了在噪声情况下利用有限规则数的模糊神经网络可以以最小的误差预测混沌系统。
The thesis uses fuzzy neural network' s approach ability to prove that a fuzzy neural network with a finite rule number can optimally predict a chaotic system with noise.
本文提出了一种混沌水文时间序列区间预测算法。
An interval prediction algorithm for chaotic hydrological time series is proposed.
如果判断出系统存在混沌现象，则设计模糊神经网络预测控制器，实现了对电力系统混沌振荡的预测控制。
If there is chaos oscillation in the power systems, a fuzzy-neural forecast controller is designed and forecast control is realized to the chaos oscillation.
如果判断出系统存在混沌现象，则设计模糊神经网络预测控制器，实现了对电力系统混沌振荡的预测控制。
If there is chaos oscillation in the power systems, a fuzzy-neural forecast controller is designed and forecast control is realized to the chaos oscillation.
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