Correlation Analysis of the Alternating Step Generator

The alternating step generator is a well-known keystream generator consisting of two stop/go clocked LFSRs, LFSR₁ and LFSR₂, whose clocks are controlled by another LFSR, LFSR₃, which is clocked regularly. A probabilistic analysis of this generator is conducted which shows that the posterior probabilites of individual bits of the first derivatives of the regularly clocked LFSR₁ and LFSR₂ sequences, when conditioned on a given segment of the first derivative of the keystream sequence, can be computed efficiently in a number of probabilistic models of interest. The expected values of these probabilities, for a random keystream sequence, are derived by an approximate theoretical analysis and are also verified by systematic computer experiments. It is pointed out that these posterior probabilities can be enhanced in a resynchronization scenario and thus used for a low-complexity fast correlation attack on the two LFSRs. More generally, it is argued that even without resynchronization these probabilities may be significantly different from one half for fast correlation attacks based on iterative decoding algorithms to be successful, although with incresead complexity. A related method for computing the posterior probabilities of individual bits of the LFSR₃ sequence, when conditioned on both the keystream sequence and the LFSR₁ and LFSR₂ sequences, is also developed. As these posterior probabilities are much more different from one half, they can be used for a low-complexity fast correlation attack on LFSR₃, provided that the initial states of LFSR₁ and LFSR₂ are previously reconstructed.     

Sufficient conditions for robust stability of large-scale dynamical systems including delayed states and parameter perturbations in interconnections

The problem of the robust stability of large-scale dynamical systems including delayed states and parameter perturbations in interconnections is considered. By using algebraic Riccati equations and some analytical methods, some sufficient conditions on linear decentralized state feedback controllers are derived so that the systems remain stable in the presence of delayed states and parameter perturbations. Such conditions give some bounds on the perturbations of interconnections with delayed states and uncertain parameters, and result in a quantitative measures of robustness for large-scale dynamical systems including delayed states and uncertain parameters in interconnections. The results obtained in this paper are applicable not only to large-scale systems with multiple time-varying delays, but also to large-scale systems without exact knowledge of the delays, i.e., large-scale systems with uncertain delays.     

无界变时滞神经网络全局稳定性

该文研究了具无界变时滞的时变神经网络的全局稳定性.利用两种不同的分析方法得到了保证这类神经网络全局渐近稳定的一些充分条件.推广和改进了现有文献中常时滞或时滞为零的相应结果.     

带有结构性不确定的线性时变系统的二次镇定及H~∞控制

本文讨论了带有结构性不确定的线性时变系统的二次镇定问题.建立了H∞指标γ和RDE及RDI之间的等价关系,并给出了带有结构性不确定线性时变系统二次稳定的充分条件;在此基础上将带有结构性不确定线性时变系统的鲁棒动态补偿器设计转化成求解一个相应的确定性辅助线性时变系统的标准H~∞控制问题.     

基于Spearman ρ的时变Copula模型的模拟及应用

本文从Spearmanρ入手,利用Spearmanρ在非线性单调变换的情况下保持不变的特点,以及与条件期望预测机制存在的非线性的关系,提出建立时变Copula的模型的新方法;通过建立时变FGM-Copula模型的实例分析表明,这种构建Copula模型的方法较好捕捉了相依机制的时变性,预测了随机变量的趋势,具有一定的优越性。     

GLOBALLY ASYMPTOTIC STABILITY OF MULTI-DELAYED NEURAL NETWORKS

A sufficient condition is presented for the uniqueness and globally asymptotic stability of a class of neural networks with multiple time-varying delays. The result is less conservative than some recent results in the literatures.     

具时变时滞的分层抑制细胞神经网络的概周期解

研究了具时变时滞的分层抑制细胞神经网络.利用不动点定理获得了若干判定该网络存在概周期解的新充分条件,改进和推广了已有文献中的相应结论.     

基于递归型神经网络动力学求解时变凸二次规划

为了在线求解时变凸二次规划问题,实现误差精度更高、求解时间更短和收敛速度更快的目标。本文采用了求解问题更快的时变网络设计参数,选择了有限时间可以收敛的Sign-bi-power激活函数,构造了一种改进的归零神经网络动力学模型。其后,分析了模型的稳定性和收敛性,得到其解能够在有限时间内收敛。最后,在仿真算例中,与传统的梯度神经网络和归零神经网络模型相比,所提模型具有更高的误差精度、更短的求解时间和更快的收敛速度,优于前两种网络模型。     

A graph-theoretic approach to exponential stability of BAM neural networks with delays and reaction-diffusion

This paper deals with the problem of global exponential stability for bidirectional associate memory (BAM) neural networks with time-varying delays and reaction-diffusion terms. By using some inequality techniques, graph theory as well as Lyapunov stability theory, a systematic method of constructing a global Lyapunov function for BAM neural networks with time-varying delays and reaction-diffusion terms is provided. Furthermore, two different kinds of sufficient principles are derived to guarantee the exponential stability of BAM neural networks. Finally, a numerical example is carried out to demonstrate the effectiveness and applicability of the theoretical results.