基于混沌遗传的巡航导弹实时规划技术

研究针对突发威胁的实时规划技术能够提高巡航导弹在未知威胁环境中的生存能力,具有重大的研究价值。实时规划的规划思想和规划算法的设计问题是巡航导弹实时规划技术必须要解决的两大难题。基于解决问题的考虑,设计了实时航迹规划的规划思路和一种混沌遗传规划算法。基于传统遗传算法的思想,利用威胁和约束条件构建适应度函数对航迹进行评价,设计了一种特殊的实数结构体编码,提出了合适的选择、交叉、变异三种算子使种群进化,并引入了混沌搜索算子,加速种群的收敛。最后进行了实时航迹规划的仿真分析。仿真结果表明:实时规划的航迹能够避开地形和新出现的威胁,算法收敛速度较快。这说明,设计的算法能够满足当前未知威胁环境下巡航导弹实时规划的需要。     

Robust parametric CMAC with self-generating design for uncertain nonlinear systems

In this paper, a robust parametric cerebellar model articulation controller (RP-CMAC) with self-generating design, called RPCSGD, is proposed for uncertain nonlinear systems. The proposed controller consists of two parts: one is the parametric CMAC with self-generating design (PCSGD), which is utilized to approximate the ideal controller and the other is the robust controller, which is designed to achieve a specified H^∞ robust tracking performance of the system. The corresponding memory size of the proposed controller can be suitably constructed via the self-generating design. Thus, the useless or untrained memories will not take possession of the space. Besides, the concept of sliding-mode control (SMC) is adopted so that the proposed controller has more robustness against the approximated error and uncertainties. The stability of the system can be guaranteed surely due to the derivations of the adaptive laws of the proposed RPCSGD based on the Lyapunov function. Finally, the proposed controller is applied to the second-order chaotic system and the one-link rigid robotic manipulator. The tracking performance and effectiveness of the proposed controller are verified by simulations of the computer.     

Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process

In the past decade, support vector machines (SVMs) have gained the attention of many researchers. SVMs are non-parametric supervised learning schemes that rely on statistical learning theory which enables learning machines to generalize well to unseen data. SVMs refer to kernel-based methods that have been introduced as a robust approach to classification and regression problems, lately has handled nonlinear identification problems, the so called support vector regression. In SVMs designs for nonlinear identification, a nonlinear model is represented by an expansion in terms of nonlinear mappings of the model input. The nonlinear mappings define a feature space, which may have infinite dimension. In this context, a relevant identification approach is the least squares support vector machines (LS-SVMs). Compared to the other identification method, LS-SVMs possess prominent advantages: its generalization performance (i.e. error rates on test sets) either matches or is significantly better than that of the competing methods, and more importantly, the performance does not depend on the dimensionality of the input data. Consider a constrained optimization problem of quadratic programing with a regularized cost function, the training process of LS-SVM involves the selection of kernel parameters and the regularization parameter of the objective function. A good choice of these parameters is crucial for the performance of the estimator. In this paper, the LS-SVMs design proposed is the combination of LS-SVM and a new chaotic differential evolution optimization approach based on Ikeda map (CDEK). The CDEK is adopted in tuning of regularization parameter and the radial basis function bandwith. Simulations using LS-SVMs on NARX (Nonlinear AutoRegressive with eXogenous inputs) for the identification of a thermal process show the effectiveness and practicality of the proposed CDEK algorithm when compared with the classical DE approach.     

一种多级混沌同步通信系统

本文了蔡氏混沌同步通信系统的主要性质和局限性,提出了一种多级混沌安全通信系统,证明了该系统对传输信号可以完全重建,在有效带宽,失真特性及安全性和方面获得了改善。文中出了给利用该系统进行语音数据和静止图像数据传输的实例。     

基于最小二乘支持向量机的衰落信道预测算法

该文探讨了利用相空间重构和支持向量机进行衰落信道非线性预测算法。该算法基于多径衰落信道具有混沌行为,利用坐标延迟理论,重建衰落信道系数的相空间,再根据混沌吸引子的稳定性和分形性,在相空间中通过递归最小二乘支持向量机(RLS-SVM)进行预测。该算法对原始数据可以进行更平滑的处理,在噪声环境下预测的时间范围更长。对时间跨度为63.829ms的衰落系数进行了预测,仿真结果表明,在信噪比为15dB时,预测结果优于AR算法。     

正交多载波降噪差分混沌键控通信系统

为解决多载波差分混沌移位键控(MC-DCSK)系统传输速率低和误码性能差的缺点,该文提出一种正交多载波降噪差分混沌移位键控(QMC-NR-DCSK)系统。在发送端,预定义载波用于发送参考信号,剩余M-1个不同中心频率的载波及其经正交调制技术后得到的频率相同但相位正交的载波都用于传输信息信号,此外,通过进一步引入Hilbert变换,将系统的频带利用率和传输速率提升为MC-DCSK系统的4倍。在接收端引入滑动平均滤波器的降噪操作降低了噪声的方差,从而改善了系统误码性能。推导了QMC-NR-DCSK系统在加性高斯白噪声(AWGN)信道和多径瑞利衰落(RFC)信道下的比特误码率公式并进行了仿真。仿真结果和理论分析表明：QMC-NR-DCSK系统能有效提升传输速率、带宽效率和误码性能,为该系统应用于多载波无线通信提供理论参考。     

一种新的含噪混沌信号降噪算法

该文针对低信噪比、非高斯加性噪声和混沌动力学系统参数未知的含噪混沌信号降噪问题,提出了一种基于粒子滤波(Particle Filtering, PF)的降噪新算法。该算法将混沌信号和动力学系统中的未知参数作为一个多维状态矢量,利用PF方法递推计算多维状态矢量的联合后验概率分布,进而实现了对混沌信号的最优估计。对于混沌信号轨道分离过快所导致的退化问题,提出了有效的解决方法,并利用核平滑和自回归(Auto-Regression, AR)模型建模的方法分别实现了非时变以及时变参数的递推估计。仿真实验的结果表明,与现有的降噪方法相比,该文提出的新算法能够更加有效地抑制含噪混沌信号中的加性噪声。     

数字混沌扩频序列的盲估计

混沌序列具有非线性、宽带类噪声、大的码族、任意长的周期且容易产生的特性,因此在扩频通信系统中很有实用价值。本文将文献[1]中对伪随机扩频序列盲估计的方法推广应用到混沌扩频通信中,并对其不足提出了改进方法。在只知道扩频码周期而无其它任何先验知识的条件下,利用特征值分析的方法可以对数字混沌直扩序列进行盲估计,仿真实验证明了该方法的有效性。     

混沌序列与一类基于移位寄存器的非线性序列的性能比较

混沌序列与一类基于移位寄存器的非线性序列非线性前馈逻辑(Non-Linear Feed-ForwardLogic,NLFFL)序列都具有非线性、宽带类噪声、大的码族、长的周期且容易产生的特性,通过对它们在产生方式、相关性能、多址性能以及抗相关攻击能力等方面进行分析和仿真研究,说明混沌序列与该类非线性序列在性能上总体相当,但对于短周期序列(N1023),混沌序列的抗相关攻击、抗干扰能力更强,因此更具有实用价值。     

DETECTING TECHNIQUE OF WEAK PERIODIC PULSE SIGNAL VIA SYNTHESIS OF CROSS-CORRELATION AND CHAOTIC SYSTEM

In this letter, with the synthesis of usual cross-correlation detecting method and chaotic detecting method, a new detecting system for the weak periodic pulse signal is constituted,in which the two methods can play respective preponderance. Theoretical analyses and simulation studies have shown that the detecting system is very sensitive to the periodic pulse signal under strong noise background and has exceedingly powerful capability of suppressing complex noise.