电离层电波信号衰减的混沌时间序列预测

电波信号穿过电离层传播时,由于电离层的作用将使得电波信号产生一系列的失真.分析了传统混沌时间序列预测的理论和方法,并分析了电波信号穿过电离层的时间序列特征,发现该时间序列具有混沌时间特征,进而在传统的混沌时间序列预测的基础上,给出了一种更适合于电离层电波衰减时间序列预测的方法.同时,利用它预测了一组电波信号穿过电离层的时间序列特征,并分析了其预测精度.结果表明,利用修正的混沌时间序列预测方法预测的电波信号穿过电离层衰减的时间序列,其短期的预测效果很好.     

混沌二进制序列性能分析

讨论了Logistic数字混沌序列的产生方法，通过与m序列比较，分析了数字混沌序列的混沌特征和相关统计特性，并用计算机进行仿真，作了相应的实验数据分析，结果表明数字混沌序列具有较理想的相关特性，是CDMA系统中扩频码的优选码型。     

基于混沌RBF网络的地下厂房围岩变形预报

引入混沌时间序列理论有效解决非线性混沌时间序列的预报问题.表征混沌特性的分维数D和最大Lya-punov指数1λ计算结果表明变形时间序列具有混沌特性;神经网络克服了模型必须是基本观测数据的线性和非线性组合的局限,具有很强的自适应性和记忆功能.结合混沌特性将一维时间序列重构为多维相空间,优化RBF神经网络结构,建立混沌时间序列神经网络预报模型.实例表明,预测值与实测值的相对误差小于6%;后期预报的变形随时间增长呈下降趋势,符合工程实际情况.     

混沌序列在测控系统中的应用方法探讨

伪随机码在测控系统中具有测距码和扩频码的双重功能。相对于常规伪随机序列,混沌序列具有一系列独有特性和优势。把混沌序列应用于测控系统中,将进一步提高系统保密性能,并改进系统功能的实现方法。分析了混沌序列的特点和优势,讨论了其在测控系统中的应用方法及关键技术,提出了测控系统中混沌序列同步的长短码转换法。该方法能够有效解决序列同步问题,并充分利用混沌序列的非周期性,解决距离测量中的模糊问题。     

一种改进的基于混沌搜索策略的小波神经网络遗传学习算法

讨论了遗传算法的优点及存在的局限性。通过引进混沌搜索策略,对遗传算法的初始种群进行混沌搜索,筛选出优化种群。并将遗传算法作为小波神经网络的学习算法,再次运用混沌搜索策略,对学习过程进行优化。改进后的遗传学习算法结合小波神经网络应用于混沌时间序列预测,实验取得了较好的效果。     

混沌序列的产生及其验证方法

介绍了几种产生混沌序列的映射函数,并给出了Ｌｙａｐｕｎｏｖ指数,它可以判定映射函数参数取何值能产生混沌序列。最后给出了检测数字序列是滞为混沌序列的方法,实验证明,该方法可以有效地对一些不具有显式映射关系的混沌序列进行验证。     

混沌理论及其在密码学的应用

由于混沌系统对初始条件和混沌参数非常敏感以及生成的混沌序列具有非周期性和伪随机性的特性，近年来混沌系统在密码学领域中得到了较多的研究。介绍了混沌学理论和现代密码学的具体内容，通过对混沌和密码学之间关系的分析。提出了把混沌用于密码学之中的具体方法和混沌密码系统的框架结构，给出了数字加密中选择混沌系统的原则。     

Logistic数字混沌序列的性能分析

针对混沌信号具有伪随机性、类噪声以及对初始条件的极端敏感性的特点 ,用定点算法有效地产生了数字混沌序列 ,着重分析了 L ogistic数字混沌序列的相关性能 ,得到了序列长度对相关性能的渐近关系式 ,并与模拟混沌序列的相关性能进行了比较 ,仿真结果表明用定点算法产生的数字混沌序列具有与模拟混沌序列一样的相关特性 ,数字量化并没有破坏混沌序列的特性。     

混沌直接序列扩频（DS／SS）通信系统

本文讨论了混沌序列在直接序列扩频通信系统中的应用。针对简单混沌序列轨迹周期短以及存在误差传播现象，提出了一种改进的混沌扩频序列产生方法。并对混沌扩频序列的特性进行了分析和研究。     

混沌直接序列扩频(DS/SS)通信系统

本文讨论了混沌序列在直接序列扩频通信系统中的应用。针对简单混沌序列轨迹周期短以及存在误差传播现象,提出了一种改进的混沌扩频序列产生方法。并对混沌扩频序列的特性进行了分析和研究。     

混沌优化在水电站经济负荷分配中的应用

介绍了混沌优化技术的原理和数学模型,在此基础上,将混沌优化技术应用于求解水电站机组之间的经济负荷分配问题,获得了与动态规划方法相同的结果．混沌方法提高了计算速度、减小了维数灾,不失为一种有生命力的优化方法．同时,还较详细地给出了混沌优化的计算步骤．     

Fuzzy controller based on chaos optimal design and its application

In order to overcome difficulty of tuning parameters of fuzzy controller, a chaos optimal design method based on annealing strategy is proposed. First, apply the chaotic variables to search for parameters of fuzzy controller, and transform the optimal variables into chaotic variables by carrier-wave method. Making use of the intrinsic stochastic property and ergodicity of chaos movement to escape from the local minimum and direct optimization searching within global range, an approximate global optimal solution is obtained. Then, the chaos local searching and optimization based on annealing strategy are cited, the parameters are optimized again within the limits of the approximate global optimal solution, the optimization is realized by means of combination of global and partial chaos searching, which can converge quickly to global optimal value. Finally, the third order system and discrete nonlinear system are simulated and compared with traditional method of fuzzy control. The results show that the new chaos optimal design method is superior to fuzzy control method, and that the control results are of high precision, with no overshoot and fast response.     

基于惯性因子的混沌粒子群优化算法研究

粒子群优化算法本身在多峰复杂函数时会出现早熟收敛现象,降低粒子的多样性,导致粒子群不能收敛到全局极值点。针对粒子群优化算法的局限性,把混沌优化思想引入到粒子群算法,采用混沌优化粒子群算法对测试函数进行仿真,并在此基础上加入惯性因子对混沌优化粒子群算法进一步改进,Matlab仿真结果表明,改进的混沌优化粒子群算法,结合了混沌和粒子群算法共同的优点,能快速、准确地搜索到全局最优值。     

基于T-S模糊模型的水轮机调节系统辨识

针对水轮机调节系统的复杂性、非线性和难以用明确数学模型表达的特点,建立了该系统的T-S模糊模型.考虑到T-S模糊模型的结构与参数间的密切关联性,提出基于混沌优化策略的结构和参数一体化辨识.该方法用混沌优化策略辨识模型的前件参数,用最小二乘法辨识模型后件参数,实现了模糊模型结构的自适应优化.试验结果表明,该T-S辨识模型具有较高的辨识精度及较强的泛化能力,实现了水轮机调节系统的有效辨识.     

Hardware/software partitioning based on dynamic combination of maximum entropy and chaos optimization algorithm

This paper presents an algorithm that combines the chaos optimization algorithm with the maximum entropy ( COA-ME) by using entropy model based on chaos algorithm,in which the maximum entropy is used as the second method of searching the excellent solution. The search direction is improved by chaos optimization algorithm and realizes the selective acceptance of wrong solution. The experimental result shows that the presented algorithm can be used in the partitioning of hardware/software of reconfigurable system. It effectively reduces the local extremum problem,and search speed as well as performance of partitioning is improved.     

减少速度更新频率的混沌粒子群算法

把速度更新策略和混沌优化相结合,提出了减少速度更新频率的混沌粒子群算法．该算法根据群体适应值的方差进行早熟收敛判断,从而使算法摆脱后期易于陷入局部最优点的束缚,同时又保持前期优秀的搜索速度的特性．通过几个基准函数测试,结果表明,新算法的性能较基本粒子群优化算法有明显的改善．     

基于混沌粒子群算法的车间作业调度优化

为提高车间作业调度效率,提出一种基于混沌粒子群算法的车间作业调度优化方法。首先以机器加工时间最短为优化目标,建立一个多约束的车间作业调度数学模型,然后采用粒子群算法对其进行求解,并通过采用混沌机制保持粒子多样性。仿真测试表明,混沌粒子群算法可以获得车间作业调度方案,具有一定应用价值。     

复杂函数优化的混沌遗传算法

将混沌融入遗传算法提出了混沌遗传算法,该方法利用混沌运动的随机性、遍历性、对初始条件的敏感性等特性进行群体的混沌初始化和最优个体的混沌变尺度载波寻优,典型复杂函数优化的仿真结果表明,该方法较遗传算法具有更快的收敛速度和更小的计算,是复杂函数优化的有效手段。     

A combination algorithm of Chaos optimization and genetic algorithm and its application in maneuvering multiple targets data association

The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones.     

Automatic pipes routing based on flow loss in electromechanical product

Based on flow loss,a new automatic pipe-routing algorithm is proposed for electromechanical product in 3D space,which consists of pre-processing and optimization search.Utilizing chaos theory,a chaos grid pre-processing model (CGPM) is established to efficiently pick up the solution space and reduce the search range in the pre-processing,which simplifies the optimization search.A modified particle swarm optimization (PSO) algorithm is presented to seek for an approximate optimal trajectory in the solution space in the optimization search based on standard PSO algorithm and migration characters of people.The comparison of experiments and analysis results shows that the modified PSO algorithm is capable of preventing prematurity effectively and searching for the optimal trajectory more efficiently.Theoretical analysis proves that the modified PSO algorithm converges at global optimum.The examples show that the automatic pipe-routing algorithm based on flow loss is effective and practical for electromechanical product.