一种新的混沌映射散列函数构造方法及应用

提出了一种基于混沌映射和乘同余法构建单向散列函数的算法. 该算法通过乘同余法生成伪随机序列作为系统的初始值, 把明文信息的美国标准信息交换码(ASCII码) 归一化后作为混沌映射的初始值, 经过可变步长的混沌数字量化后, 提取出128 bit的散列值. 理论分析和仿真结果表明: 该算法具有较好的不可逆性、 抗碰撞性、 防伪造性、 初值敏感性以及较高地运行速度.     

一类基于混沌映射构造Hash函数方法的碰撞缺陷

混沌动力学系统在一定的参数范围内出现混沌运动，且它所产生的混沌序列具有良好的伪随机特性.以混沌映射为基础，已有文献提出了一些构造单向散列函数的方法.对这些方法进行分析，证明它们均存在碰撞缺陷，并总结了采用混沌映射构造单向散列函数时应注意的问题. 关键词： 混沌 单向散列函数 碰撞     

One-way hash function construction based on the spatiotemporal chaotic system

Based on the spatiotemporal chaotic system,a novel algorithm for constructing a one-way hash function is proposed and analysed.The message is divided into fixed length blocks.Each message block is processed by the hash compression function in parallel.The hash compression is constructed based on the spatiotemporal chaos.In each message block,the ASCII code and its position in the whole message block chain constitute the initial conditions and the key of the hash compression function.The final hash value is generated by further compressing the mixed result of all the hash compression values.Theoretic analyses and numerical simulations show that the proposed algorithm presents high sensitivity to the message and key,good statistical properties,and strong collision resistance.     

Hash function based on chaotic map lattices

A new hash function system, based on coupled chaotic map dynamics, is suggested. By combining floating point computation of chaos and some simple algebraic operations, the system reaches very high bit confusion and diffusion rates, and this enables the system to have desired statistical properties and strong collision resistance. The chaos-based hash function has its advantages for high security and fast performance, and it serves as one of the most highly competitive candidates for practical applications of hash function for software realization and secure information communications in computer networks.     

Collision analysis of one kind of chaos-based hash function

In the last decade, various chaos-based hash functions have been proposed. Nevertheless, the corresponding analyses of them lag far behind. In this Letter, we firstly take a chaos-based hash function proposed very recently in Amin, Faragallah and Abd El-Latif (2009) [11] as a sample to analyze its computational collision problem, and then generalize the construction method of one kind of chaos-based hash function and summarize some attentions to avoid the collision problem. It is beneficial to the hash function design based on chaos in the future.     

不确定混沌系统的径向基函数神经网络反馈补偿控制

对不确定混沌系统控制问题, 研究了一种基于径向基函数神经网络(radial basis function neural network, RBFNN)的反馈补偿控制方法. 该方法首先用RBFNN对混沌系统的动力学特性进行学习, 然后用训练好的RBFNN模型对混沌系统进行反馈补偿控制. 该方法的特点是不需要被控混沌系统的数学模型,可以快速跟踪任意给定的参考信号. 数值仿真试验表明了该控制方法不仅具有响应速度快、控制精度高, 而且具有较强的抑制混沌系统参数摄动能力和抗干扰能力.     

最近邻耦合网络的时空混沌同步研究

本文进行了最近邻网络的时空混沌同步研究.以时空混沌系统作为网络的节点,基于Lyapunov稳定性定理,通过确定网络的最大Lyapunov指数,得到了实现网络完全同步的条件.采用Fisher-Kolmogorov时空混沌系统作为网络节点实例进行了仿真模拟,获得了理想的同步效果.进一步研究了有界噪声影响下网络的同步性能,结果显示它具有较强的抗干扰能力.     

基于改进的径向基函数神经网络的铁磁谐振系统混沌控制

面向中性点直接接地电力系统发生的铁磁谐振过电压所显现的混沌特性，在径向基函数神经网络的基础上，提出引进一种极大熵学习算法对该混沌系统进行控制.该方法通过最优化一个目标函数导出中心向量的学习规则，充分利用网络隐层的聚类功能，极大改善网络的回归和学习能力.对具体的铁磁谐振系统的数值实验证实了该方法在针对铁磁谐振过电压混沌控制中的有效性和可行性. 关键词： 中性点直接接地系统 混沌控制 径向基函数 极大熵原理     

Prediction based chaos control via a new neural network

In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network.     

一种基于混沌的带密钥Hash函数的碰撞问题及分析

指出了一类基于混沌映射构造带密钥单向hash函数算法的碰撞问题,并对其产生的机理进行了初步分析,给出了数字化混沌序列非奇异的定义,证明了数字化混沌序列非奇异的充要条件,并分析了变参数离散混沌动力系统数字化后序列的周期性.分析结果表明这类算法产生碰撞的原因是其对混沌映射的数字化导致混沌序列的奇异性,因此必须谨慎选择混沌映射的数字化方法以保证混沌序列的非奇异性. 关键词： 混沌 带密钥散列函数 碰撞 非奇异性     

混沌神经网络的动态阈值控制

提出了混沌神经网络的动态阈值控制方法, 将大脑脑电波的主要成分, 正弦信号作为控制变量实现对混沌神经网络内部状态的阈值动态改变, 从而达到了控制混沌神经网络混沌的目的. 利用该方法可以将混沌神经网络的输出稳定在一个与网络初始模式相关的存储模式和其反相模式上, 从而使混沌神经网络在模式识别、信息搜索等信息处理功能得以实现. 该控制方法不需要事先指定阈值, 是一种自适应方法, 符合实际人脑的思维运动. 关键词： 混沌控制 混沌神经网络 动态阈值控制 信息处理     

Parallel keyed hash function construction based on chaotic maps

Recently, a variety of chaos-based hash functions have been proposed. Nevertheless, none of them works efficiently in parallel computing environment. In this Letter, an algorithm for parallel keyed hash function construction is proposed, whose structure can ensure the uniform sensitivity of hash value to the message. By means of the mechanism of both changeable-parameter and self-synchronization, the keystream establishes a close relation with the algorithm key, the content and the order of each message block. The entire message is modulated into the chaotic iteration orbit, and the coarse-graining trajectory is extracted as the hash value. Theoretical analysis and computer simulation indicate that the proposed algorithm can satisfy the performance requirements of hash function. It is simple, efficient, practicable, and reliable. These properties make it a good choice for hash on parallel computing platform.     

基于视神经网络的混沌时间序列奇异信号实时检测算法

提出了一种基于视神经网络的实时检测混沌时间序列中的奇异点算法,设计了视神经网络奇异点检测器(RNNND);然后设计了基于反向传播(BP)神经网络和径向基函数(RBF)神经网络的混沌时间序列奇异点检测器.利用Lorenz理论模型产生的时间序列和实测输油管道压力时间序列分别检验了这3个奇异点检测器在抗干扰能力、检测微弱信号能力和运算速度等方面的性能.仿真和分析表明,RNNND具有良好的检测精度和较快检测速度.最后详细分析了3种奇异点检测器优缺点并给出了适用场合.     

Security analysis of a one-way hash function based on spatiotemporal chaos

The collision and statistical properties of a one-way hash function based on spatiotemporal chaos are investigated. Analysis and simulation results indicate that collisions exist in the original algorithm and, therefore, the original algorithm is insecure and vulnerable. An improved algorithm is proposed to avoid the collisions.     

A study of network dynamics

The dynamics of a discrete-time neural network model are investigated. First, a numerical survey of network power spectra is reported for networks of varying size with random weight matrices and initial states. The steepness of the logistic function and a symmetry measure of the weight matrix are taken as control parameters. Summary statistics are presented to give gross measures of the model's temporal activity in parameter space. Second, a detailed study of the dynamics of a particular network is described. Complex dynamical behavior is observed, including Hopf bifurcations, the Ruelle-Takens-Newhouse route to chaos (showing mode-locking at rational winding numbers and the destruction of an invariant torus), and the period-doubling route to chaos.     

基于神经网络的托卡马克能量约束时间预测

使用机器学习理论中的神经网络方法,根据通用逼近原理对能量约束时间的复杂函数进行逼近,采用托卡马克装置的典型实验数据,设计一种组合结构的神经网络。通过大量的调参试验,给出一套性能最好的参数组合,并与传统幂指数形式的多元线性回归方法进行准确性和数据集迁移能力的比较。结果表明：神经网络模型对于能量约束时间的预测准确率更高,回归性能更好,且具有一定的抗噪声能力,更适合作为能量约束时间的定标或预测工具。     

迟滞混沌神经元/网络的控制策略及应用研究

为了保持神经网络在优化计算求解过程中结构不被改变, 以迟滞混沌神经元和迟滞混沌神经网络为研究对象, 提出了一种基于滤波跟踪误差的控制策略来实现神经元/网络的稳定控制. 采用该控制策略, 在不改变非线性特性发生机理的情况下, 神经元/网络可实现函数优化计算问题的求解. 所设计的控制律包含两部分: 一部分是系统进入滤波跟踪误差面时的等效控制部分, 另一部分为确保系统快速进入滤波跟踪误差面的控制部分. 采用Lyapunov方法对神经元/网络的控制进行了稳定性证明. 根据待寻优函数直接求得神经元的控制律, 在该控制律的作用下, 神经元/网络可逐渐稳定到优化函数的极值点, 从而实现优化问题的求解, 仿真实验结果验证了该控制方法在优化计算中的可行性和有效性.     

Chaotic diagonal recurrent neural network

We propose a novel neural network based on a diagonal recurrent neural network and chaos,and its structure and learning algorithm are designed.The multilayer feedforward neural network,diagonal recurrent neural network,and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map.The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks.     

Liley模型的模拟EEG信号的非线性预测和分析

分析Liley模型的模拟脑电(Electroencephalogram,EEG)信号的非线性预测和径向基函数(Radial Basis Functions,RBF)神经网络预测,利用相图分析和非线性正交预测(Nonlinear Cross-Prediction,NLCP)方法研究模拟EEG信号.结果发现:①RBF神经网络预测的效果要好于非线性预测;②NLCP方法对含有强周期分量的高维系统具有较好的适用性;③支持了EEG中存在混沌运动的观点.     

Chaos control of ferroresonance system based on RBF-maximum entropy clustering algorithm

With regards to the ferroresonance overvoltage of neutral grounded power system, a maximum-entropy learning algorithm based on radial basis function neural networks is used to control the chaotic system. The algorithm optimizes the object function to derive learning rule of central vectors, and uses the clustering function of network hidden layers. It improves the regression and learning ability of neural networks. The numerical experiment of ferroresonance system testifies the effectiveness and feasibility of using the algorithm to control chaos in neutral grounded system.