初始条件敏感性

TheButterflyEffect《蝴蝶效应》蝴蝶效应是混沌理论中的一个重要概念，它是指对初始条件敏感性的一种依赖现象：输入端微小的差别会迅速放大到输出端。

The title refers to the metaphorical butterfly effect , a popular principle in chaos theory which states that in any dynamic system , small initial differences may over time lead to large unforeseen consequences 。

它的初始条件敏感性，伪随机特性与宽功率谱特性使其在保密通信领域有着巨大的潜力。

Its sensitivity to initial conditions , pseudo-random features and a wide power spectrum of characteristics that a confidential communication has great potential .

对实验结果感到非常好奇，洛伦兹开始尝试用数学原理解释大型复杂的系统（如气候）对初始条件敏感性的依赖现象。

Intrigued by the results , Lorenz began creating a mathematical explanation that would show the sensitive dependence of large , complex systems like the weather .

SOFM算法比起LBG算法在矢量量化码本形成方面有许多的优点，初始条件的敏感性低，能够产生较低平均失真的码本，在图像向量编码中得到广泛的应用。

The SOFM algorithm compares the LBG algorithm this to form the aspect in the vector quantization code to have many merits , the initial condition sensitivity is low , can have the codebook which low distorts equally this , obtains the widespread application in the image vector coding .

混沌的特性主要有伪随机性、遍历性和对初始条件的敏感性。

Chaos has the following main characteristics : quasi-randomness ;

并购绩效对初始条件的敏感性使得传统的线性研究范式已经失效。

MA performance sensitivity to initial conditions makes the traditional linear paradigm has been ineffective .

四川盆地西部暴雨对初始水汽条件敏感性的模拟研究

Numerical Simulation on the Sensitivity of Heavy Rainfall over the Western Sichuan Basin to Initial Water Vapor Condition

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 .

混沌信号对初始条件的敏感性和似随机性的特点，非常有利于应用在保密通信系统中。

These chaotic characters of sensitive dependence on initial conditions and random-like behavior make the chaos signal be fit for applying in secure communication .

以混沌神经网络为基础，研究其非线性动力学特性、混沌吸引子轨迹以及对初始条件的敏感性，实现混沌神经网络的动态联想记忆功能。

Dynamic associative memory and essential characteristics of chaotic neural network is dealt with : nonperiodic chaos , chaotic attractors , and sensitivity to starting condition .

混沌是一门新兴科学，由于其有对初始条件的敏感性、貌似随机的行为、连续宽带功率谱等特征，它有很多潜在的应用价值，因此混沌成为了一门热门的话题。

Chaos is a newly developed subject . Because of its characteristics of sensitivity to initial condition , it has many potential applications . So chaos has become a popular topic .

混沌系统具有不确定性、伪随机性、不可预测性及对初始条件的敏感性等基本特性，因而能够很好的满足密码学的要求，非常适合作为保密通信的载体对数字图像进行加密处理。

Chaotic systems , with the basic characteristics of uncertain , pseudo-randomness , unpredictability and sensitivity to initial conditions , can well meet the requirements of cryptography , therefore , it is very suitable as a carrier of secure communication to encrypt digital image .

由于非周期性连续带宽频谱是混沌信号所具有的特点，并且它还有类似噪声以及对初始条件的极端敏感性等特点，非常有利于应用在保密通信系统中。

Chaotic signal has the aperiodic continuous bandwidth spectrum , noise-like and the sensitivity on initial value . These properties make the chaos signal be fit for applying in secure communication and information encrypting .

而混沌映射具有伪随机性、遍历性、对初始条件的高度敏感性等良好特性，这些特性与密码学中的扩散和混淆原则联系紧密，使得其特别适合图像加密。

Chaotic maps have many desirable properties such as pseudo-randomness , ergodicity , high sensitivity to initial conditions , and these properties have tight link with the two principles of the cryptography that is confusion and diffusion .

混沌动力系统对初始条件的极端敏感性，能产生大量的非周期、连续宽带频谱、似噪声同时可确定可再生的混沌信号，因而混沌系统特别适用于保密通信领域。

The chaotic system is highly sensitive to their initial conditions , so it can generate a large number of contiuous broadband frequency spectrum , non-periodic , noise-like , yet deterministic and reproducible signals , which is very useful for secure communications .

提出一种基于混沌迭代映射的动态分组密码加密算法，充分利用了混沌的遍历性、伪随机性及对初始条件和参数的敏感性等特性。

Based on the properties of chaos such as the random-like behavior , ergodicity , and the sensitive dependence on initial conditions and parameters , a new block encryption cipher is presented .