Local Prediction of Chaotic Time Series Based on Support Vector Machine

Based on phase space delay-coordinate reconstruction of a chaotic dynamics system, we propose a local prediction of chaotic time series using a support vector machine （SVM） to overcome the shortcomings of traditional local prediction methods. The simulation results show that the performance of this proposed predictor for making onestep and multi-step prediction is superior to that of the traditional local linear prediction method and global SVM method. In addition, it is significant that its prediction performance is insensitive to the selection of embedding dimension and the number of nearest neighbours, so the satisfying results can be achieved even if we do not know the optimal embedding dimension and how to select the number of nearest neighbours.     

ADAPTIVE NONLINEAR FEEDBACK CONTROL OF CHAOTIC SYSTEMS BASED ON REDUCED PARAMETER QUADRATIC PREDICTORS

An adaptive nonlinear feedback-control method is proposed to control continuous-time chaotic dynamical systems, where the adaptive nonlinear controller acts on only one-dimensional error signal between the desired state and the observed chaotic state of a system. The reduced parameter adaptive quadratic predictor used in adaptive feedback cancellation of the nonlinear terms can control the system to any desired state. Computer simulation results on the Lorenz system are shown to demonstrate the effectiveness of this feedback-control method.     

一种快速稀疏分解图像去噪新方法*

提出了一种基于分层树型结构正交匹配追踪算法的快速图像去噪方法.通过选择高斯函数和墨西哥草帽小波母函数构建混合冗余字典,采用分层树状结构表示字典,结合构正交匹配追踪算法,实现图像稀疏表示,提高了图像表示的稀疏性,降低了算法的复杂度.依据噪音能量阈值,通过多次迭代达到图像去噪的目的.实验结果表明,在相同的噪音水平下,该迭代去噪算法取得了较高的较好的PSNR,获得更好的视觉效果.     

基于复合非线性数字滤波器的Hash函数构造

在对多个满足Kelber条件的滤波器组成的复合系统进行初步分析的基础上，提出了一个基于复合非线性数字滤波器的带密钥的Hash算法.算法首先构建能产生高维混沌序列的复合滤波器系统，然后在明文作用的复合序列控制下随机选择滤波器子系统，并以复合系统的初态作为密钥，以粗粒化的量化迭代轨迹作为明文的Hash值.讨论了复合系统实现Hash函数的不可逆性、防伪造性、初值敏感性等特点.研究结果表明：基于复合非线性数字滤波器的Hash算法简单快速，比基于单一混沌映射的Hash算法有着更高的安全性，同时滤波器结构中没有复杂的浮点运算，比一般复合混沌系统更易于软硬件实现. 关键词： Hash 函数 混沌 非线性自回归数字滤波器     

FAST EVOLVING MULTI-LAYER PERCEPTRONS FOR NOISY CHAOTIC TIME SERIES MODELING AND PREDICTIONS

A fast evolutionary programming (FEP) is proposed to train multi-layer perceptrons (MLP) for noisy chaotic time series modeling and predictions. This FEP, which uses a Cauchy mutation operator that results in a significantly faster convergence to the optimal solution, can help MLP to escape from local minima. A comparison against back-propagation-trained networks was performed. Numerical experimental results show that the FEP can help MLP better capturing dynamics from noisy chaotic time series than the back-propagation algorithm and produce a more consistently modeling and prediction.     

基于空间伸缩结构的参数可控的混沌Hash函数

结合并行Hash函数和多混沌的设计思想,提出了一种基于空间伸缩结构的参数可控的混沌Hash函数构造方法.该方法结合空间结构的伸缩特性,使用动态密钥控制消息在空间的"膨胀-收缩-置乱"方式,有效地提高了系统的混乱和扩散特性,同时使用空间并行结构在提高Hash函数安全性的同时也提高了系统的执行效率.研究结果表明:新算法的并行计算速度快,且产生的Hash序列满足均匀分布,具有更为理想的混淆与扩散特性.     

Embedding adaptive arithmetic coder in chaos-based cryptography

In this study an adaptive arithmetic coder is embedded in the Baptista-type chaotic cryptosystem for implementing secure data compression. To build the multiple lookup tables of secure data compression, the phase space of chaos map with a uniform distribution in the search mode is divided non-uniformly according to the dynamic probability estimation of plaintext symbols. As a result, more probable symbols are selected according to the local statistical characters of plaintext and the required number of iterations is small since the more probable symbols have a higher chance to be visited by the chaotic search trajectory. By exploiting non-uniformity in the probabilities under which a number of iteration to be coded takes on its possible values, the compression capability is achieved by adaptive arithmetic code. Therefore, the system offers both compression and security. Compared with original arithmetic coding, simulation results on Calgary Corpus files show that the proposed scheme suffers from a reduction in compression performance less than 12{\%} and is not susceptible to previously carried out attacks on arithmetic coding algorithms.     

连续混沌系统的非线性自适应预测跟踪控制

基于混沌时间序列的非线性自适应预测原理,用一种sigmoid-Volterra自适应预测滤波器研究了连续混沌系统的非线性自适应预测跟踪控制.通过对Lorenz,R?ssler等典型混沌系统的控制,仿真证实了这种sigmoid-Volterra自适应预测控制器的有效性.这种方法的优点在于它既不需要知道精确的混沌系统模型,也不需要进行系统模型辨识. 关键词： 混沌 sigmoid-Volterra自适应预测滤波器 非线性自适应预测跟踪     

A novel chaotic stream cipher and its application to palmprint template protection

Based on a coupled nonlinear dynamic filter (NDF), a novel chaotic stream cipher is presented in this paper and employed to protect palmprint templates. The chaotic pseudorandom bit generator (PRBG) based on a coupled NDF, which is constructed in an inverse flow, can generate multiple bits at one iteration and satisfy the security requirement of cipher design. Then, the stream cipher is employed to generate cancelable competitive code palmprint biometrics for template protection. The proposed cancelable palmprint authentication system depends on two factors: the palmprint biometric and the password/token. Therefore, the system provides high-confidence and also protects the user's privacy. The experimental results of verification on the Hong Kong PolyU Palmprint Database show that the proposed approach has a large template re-issuance ability and the equal error rate can achieve 0.02%. The performance of the palmprint template protection scheme proves the good practicability and security of the proposed stream cipher.