提出了一种新型的高维混沌同步控制方法。
A novel method of high dimensional chaos synchronous control is stated.
但降维方法丢失了高维混沌的部分信息，存在着一定的局限性。
At present, reducing dimension is the leading method to study high-dimensional chaos, but such methods is limited due to losing some information.
但是当前的研究多数是针对低维混沌信号，本文对高维混沌特性的脑电信号的预测研究作初步探讨。
However, most existing research results are main about the prediction of the low-dimension chaotic signal, we investigate the prediction of high-dimension chaotic EEG signal in this paper.
本文针对传统的单一混沌序列安全性较差的缺点，提出了一种基于参数切换的高维混沌序列产生方法。
A multidimensional chaotic stream cipher generator based on the nonlinear autoregressive filter structure with parameter switching is proposed in this paper.
根据高维混沌系统具有更高安全性的特点，提出一种基于统一混沌系统和广义猫映射的彩色图像加密新算法。
According to the characteristic of higher secrecy of high-dimension chaotic system, a new colour image encryption algorithm based on unified chaotic system and general cat maps was proposed.
一是提出了一种高维随机梅尔·尼科夫推广方法，这一方法使得我们顺利利用合适的噪声扩大系统混沌窗口成为可能。
One is the stochastic extended form of the high-dimension Melnikov method which make it possible to choose proper noise excitation to extend the chaos window of a dynamical system.
并根据非直接耦合的蔡氏电路，观测到了高维超混沌系统的行为特征。
According to indirectly coupled Chua's circuit, we can observe high dimension hyperchaotic system's characteristic of behavior.
脑电(EEG)信号具有高维的混沌特性，混沌信号的预测是当前混沌理论与应用研究的一个重要方向。
EEG signal is characterized by high-dimension chaotic. The prediction of chaotic signal is an importance area of chaos theory and its application.
把对有限维离散化系统混沌控制的方法应用于高维微分动力系统，讨论了双光子光学双稳系统中的混沌控制。
Applying the method of controlling chaos presented by documents to a high dimensional differential dynamical system, the control of two photon optical bistable system's chaos is discussed.
计算结果表明，该方法对高维微分动力系统中的混沌控制是有效的。
The numerical calculation shows that it is effective to use the method in the control of a high dimensional differential dynamical system.
基于混沌神经网络模型可以有效地解决高维、离散、非凸的非线性约束优化问题。
The Chaotic neural network model can be used to solve many multi-dimensioned, discrete, non-convex, nonlinear constrained optimization problems.
基于混沌神经网络模型可以有效地解决高维、离散、非凸的非线性约束优化问题。
The Chaotic neural network model can be used to solve many multi-dimensioned, discrete, non-convex, nonlinear constrained optimization problems.
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