基于原生熵的Logistic映射复杂性分析

在序列原生生成过程的基础上,提出了原生熵的概念来分析离散混沌映射的复杂性。先对实值混沌映射量化成二值混沌序列,然后计算其原生熵的大小,并以Logistic映射为例证明了此算法的有效性。仿真结果表明此方法能反映离散混沌系统的随机本质,可用于准确度量离散混沌系统的复杂性强弱,且计算简单。     

Logistic混沌序列叠加过程分解模型与弱信号高精度复原

本文研究对由若干个Logistic混沌或非混沌序列叠加过程形成的有成因联系的多变量时间序列的弱信号进行高精度复原的分解模型和算法,通过数字仿真分析表明该分解模型和算法对Logistic混沌或非混沌序列弱信号具有高精度地复原的性能.     

混沌时间序列的遗传演化建模

从实测时间序列中构造混沌系统的模型是非线性时间序列分析中的一个重要议题,本文利用遗传编程方法(GP),在尽可能大的函数空间内搜索合适的模型结构,并引入粒子群算法(PSO)在动态模型结构情况下进行非线性参数估计,取得了较好效果.此外,演化建模的实现结合了非线性时间序列分析(NTSA)的结果,以NTSA的结果指导演化建模参数的选取并作为模型优劣的评判标准,改进了经典GP算法对混沌系统建模的应用效果.     

一种新的基于复合混沌模型的图像加密算法

论文提出了一种新的基于包括Logistic映射模型和混合光学双稳模型的复合混沌系统的图像加密算法。随着对外部密钥的映射,将选择相应的混沌动力系统,产生出混沌序列,对原始图像加密掩盖。计算机仿真的结果表明,混沌序列对外部密钥很敏感。该算法具有良好的运算效率,加密图像具有良好的统计特性。     

一种改进的混沌图像加密算法

为了减轻有限精度效应对混沌特性的影响,在此提出了一种基于混沌扰动的改进算法.首先讨论量化误差对Logistic混沌特性的影响,提出基于混沌扰动的Logistic映射实现,通过混沌序列随机改变混沌映射的输出.对改进算法的分析表明,混沌序列的随机性和周期性方面比改进前有了明显的改善.最后提出基于混沌扰动的图像加密算法,仿真表明,图像的保密性能大大增强.     

基于混沌的图像加密算法研究

利用混沌序列的特性,提出了一种新的混沌图像加密算法.首先由Logistic混沌映射生成混沌序列,再结合灰度值替代方法对图像进行加密,然后进行实验仿真和性能分析,结果表明该算法具有一定的有效性和良好的加密性能.     

基于混沌理论的煤矿监控网络流量时间序列的预测

针对目前煤矿监控网络的流量增大趋势,为了改进和提高网络的QoS质量,提出了基于混沌时间序列预测网络流量的方法.从相空间重构,用互信息量法和虚假临近点法确定了延迟时间和嵌入维数,用小数据量法求解了最大Lyapunov指数,由此证明了网络流量时间序列的混沌特性,并且建立相应模型,成功地对其做出了预测.仿真结果表明,该方法具有较高的准确度.     

基于C0算法的混沌系统复杂度特性分析

采用C0复杂度算法,分析了Logistic映射、简化Lorenz系统和超混沌Lorenz系统的复杂度特性,并与系统的Lyapunov指数谱和分岔图进行对比,结果表明,C0复杂度能正确反映系统的复杂度特性;三系统复杂度从大到小依次为Logistic系统、超混沌Lorenz系统和简化Lorenz系统。将C0复杂度算法与谱熵算法（SE ）和强度统计算法（LMC ）计算结果对比,进一步说明C0算法分析混沌系统复杂度的有效性。系统复杂度随时间演化的特性分析表明,系统复杂度在一定范围内波动,即系统具有演化稳定性,两连续系统中 y序列复杂度最大。为混沌系统应用于信息加密、保密通信领域提供了理论与实验依据。     

基于混沌优化的无线定位算法研究

本文提出一种抗NLOS干扰的TDOA定位算法。基于混沌优化算法对TDOA算法做了改进,采用混沌优化算法计算初值,再利用Taylor级数展开法进行定位。根据产生混沌序列的两种映射Logistic映射和Kent映射的特点,提出了Kent-Logistic模型修正Logistic映射用于定位。在移动台距服务基站较近时,直接采用该模型进行定位。通过和传统蜂窝定位算法的仿真结果进行比较,表明提出的算法能够提高NLOS传播环境下的定位精度。     

基于相互迭代混沌算法实现数据加密及算法对比

采用了Logistic和Henon的相互迭代的复合混沌的模型,通过混沌序列的优化和置乱算法完成混沌加密的设计。算法改进结果表明:其保证了安全性的同时实现了较快的加密速度;解密图像效果发现算法有很强的抗攻击能力。通过和传统的空域复合加密算法进行对比得出改进算法在抗密钥穷举攻击的能力、加密速度比、破译的难度等方面具有优势。     

基于神经网络混沌扩频序列的研究

应用神经网络的强大学习能力和具有全局最优的BP改进算法,提出了通过训练学习建立的具 有混沌性态的优化神经网络模型;利用网络权值调整的灵活性来产生混沌序列,该模型序列更换容易并且数 量巨大。实验与分析结果表明该模型产生的混沌扩频序列具有良好的相关特性、平衡特性以及理想的线性复 杂度,是最优加密密钥及扩频码的优选码型之一。     

具有时延的神经元模型耦合系统的混沌同步

该文研究了一个由有混沌现象的具有时延的简单神经元模型构成的耦台系统的混沌同步,具有时延的神经元模型混沌系统,对时延的初始值具有敏感性,对于任意激活函数,用解析分析方法得到了耦合系统达到强同步的一般化条件。     

Traffic modelling and forecasting using genetic algorithms for next-generation cognitive radio applications

This article presents a genetic-algorithm-based prediction model for forecasting traffic demands of next-generation wireless networks that are expected to be chaotic in nature. The model approximates the best-fit mathematical equation that generates a given time series using a genetic algorithm. It estimates future traffic in wireless networks using the most recent traffic data points collected from the actual network. Such estimations will be beneficial for network operators helping to manage and optimise the limited radio resources efficiently and eventually to facilitate cognitive radio applications. The new model is compared with conventional regressions analysis and exponential smoothing models, and it has been found that the genetic algorithm model successfully recovers the underlying mathematical expression describing chaotic time series in less than 200 generations and the predictions achieved are by far better than those of regression and exponential smoothing models. The model also offers benefits for in cognitive communication systems with their intrinsic learning capabilities and distributed access decisions.     

Prediction of chaotic time series based on the recurrent predictor neural network

Chaos limits predictability so that the long-term prediction of chaotic time series is very difficult. The main purpose of this paper is to study a new methodology to model and predict chaotic time series based on a new recurrent predictor neural network (RPNN). This method realizes long-term prediction by making accurate multistep predictions. This RPNN consists of nonlinearly operated nodes whose outputs are only connected with the inputs of themselves and the latter nodes. The connections may contain multiple branches with time delays. An extended algorithm of self-adaptive back-propagation through time (BPTT) learning algorithm is used to train the RPNN. In simulation, two performance measures [root-mean-square error (RMSE) and prediction accuracy (PA)] show that the proposed method is more effective and accurate for multistep prediction. It can identify the systems characteristics quite well and provide a new way to make long-term prediction of the chaotic time series.     

基于动态加权LS-SVM的网络流量混沌预测

网络流量是具有复杂非线性、不确定时变性的混沌时间序列.为提高标准最小二乘支持向量机的预测精度与自适应性,提出一种基于动态加权最小二乘支持向量机的网络流量混沌预测方法.该方法在标准LS-SVM回归机的训练样本误差设置时间权,增强对非线性样本的逼近能力.然后结合滚动窗与迭代求逆法实现模型动态在线校正,进而克服网络变化时的累积误差.仿真实验结果表明,相对常规LS-SVM,该模型能降低预测误差、减少计算时间,实现高精度实时混沌流量估计.     

Robust time delay estimation of bioelectric signals using least absolute deviation neural network

The time delay estimation (TDE) is an important issue in modern signal processing and it has found extensive applications in the spatial propagation feature extraction of biomedical signals as well. Due to the extreme complexity and variability of the underlying systems, biomedical signals are usually nonstationary, unstable and even chaotic. Furthermore, due to the limitations of the measurement environments, biomedical signals are often noise-contaminated. Therefore, the TDE of biomedical signals is a challenging issue. A new TDE algorithm based on the least absolute deviation neural network (LADNN) and its application experiments are presented in this paper. The LADNN is the neural implementation of the least absolute deviation (LAD) optimization model, also called unconstrained minimum L1-norm model, with a theoretically proven global convergence. In the proposed LADNN-based TDE algorithm, a given signal is modeled using the moving average (MA) model. The MA parameters are estimated by using the LADNN and the time delay corresponds to the time index at which the MA coefficients have a peak. Due to the excellent features of L1-norm model superior to Lp-norm (p > 1) models in non-Gaussian noise environments or even in chaos, especially for signals that contain sharp transitions (such as biomedical signals with spiky series or motion artifacts) or chaotic dynamic processes, the LADNN-based TDE is more robust than the existing TDE algorithms based on wavelet-domain correlation and those based on higher-order spectra (HOS). Unlike these conventional methods, especially the current state-of-the-art HOS-based TDE, the LADNN-based method is free of the assumption that the signal is non-Gaussian and the noises are Gaussian and, thus, it is more applicable in real situations. Simulation experiments under three different noise environments, Gaussian, non-Gaussian and chaotic, are conducted to compare the proposed TDE method with the existing HOS-based method. Real application experiment is conducted to extract time delay information between every two adjacent channels of gastric myoelectrical activity (GMA) to assess the spatial propagation characteristics of GMA during different phases of the migrating myoelectrical complex (MMC).     

一种新的混沌扩频序列产生方法

本文提出了一种新的混沌扩频序列产生方法。该方法基于神经网络的强大学习能力和副近非线性函数能力,应用具有全局最优的ＢＰ改进算法通过训练学习建立起具有混沌性态的优化神经网络模型,利用网络权值调整的灵活性来产生混沌扩频序列。计算机仿真结果表明,该模型产生的混沌扩频序列调整更容易,比基于单一混沌映射能产生更多符合扩频通信要求的扩频序列。     

基于混沌时间序列和神经网络的网络流量预测方法

网络流量时间序列具有复杂的非线性和不确定性特征,故提出以相空间重构理论与递归神经网络相结合的网络流量预测方法。以相空间重构理论确定最佳延迟时间和最小嵌入雏数,重构网络流量时间序列。将重构后的时间序列运用递归神经网络来训练,得到合适的模型,并用于网络节点中网络流量的预测。将该方法应用于实际数据预测,其结果与传统的时间序列预测方法结果相比较,提高了预测精度和稳定性,证明了该预测模型和方法在实际时间序列预测领域的有效性和实用性。     

混沌背景中微弱信号检测的回声状态网络方法

对复杂非线性系统的相空间重构理论进行了研究分析,提出了混沌背景中微弱信号检测的回声状态网络方法。针对回声状态网络模型参数选取困难这一问题,采用遗传算法对其模型参数进行优化。将回声状态网络模型参数作为遗传算法的个体,混沌时间序列预测均方根误差的倒数作为适应度函数,通过选择、交叉、变异等操作获得适合数据特点的最优模型参数。根据回声状态网络强大的学习和非线性处理能力,利用得到的回声状态网络模型最优参数建立混沌背景噪声的单步预测模型,将淹没在混沌背景噪声中的微弱瞬态信号和周期信号从预测误差中检测出来。以Lorenz系统和实测的海杂波数据作为混沌背景噪声进行仿真实验,仿真结果表明,本文所提方法在预测精度和训练速度方面均优于支持向量机和神经网络模型,能够有效地检测出混沌背景噪声中的微弱目标信号,且具有较小的预测误差。