含时延的联想记忆神经网络的指数吸引域和指数收敛速度

采用不等式技巧和非负矩阵性质, 给出了含时延的联想记忆神经网络平衡点的指数吸引域和指数收敛速度估计以及指数稳定的一些判断条件.     

基于指数型多延迟误差信号自相关的变步长常模算法

为了进一步提高指数型变步长常数模算法收敛速度,在分析误差信号自相关性的基础上,利用多延迟误差信号的自相关函数来控制步长,提出一种基于指数型多延迟误差信号自相关的变步长常模算法。该算法与无延迟及单位延迟相比,多延迟误差信号的自相关函数可以为训练轨迹提供简单且更为准确的信息,使得算法的收敛速度更快,同时使收敛过程更加平滑稳定。水声信道仿真实验进一步说明了该算法在收敛速度上的优越性。     

一种改进指数趋近律单向辅助面滑模控制

针对连续非线性系统,结合指数趋近律,采用一种改进指数趋近律的单向辅助面滑模控制方法。首先依据给定的连续非线性系统和状态约束,设计基于指数趋近律的单向辅助面滑模控制器,在此基础上,引入了改进指数趋近律单向辅助面滑模控制器,并给出有限时间收敛的理论证明和收敛时间计算公式。最后通过仿真验证了改进的指数趋近律单向辅助面滑模控制方法具有更快的收敛速度和更有效的抖振抑制能力。     

Lebesgue-p范数意义下区间可调节的变增益加速迭代学习控制

为加快迭代学习控制律的收敛速度,针对线性时不变(LTI)系统,以PD-型学习律为例,提出一种区间可调节的具有指数加速的迭代学习控制算法.首先,根据每次学习效果确定下一次迭代需要修正的区间并在该区间内修正控制律增益;然后,在Lebesgue-p范数意义下分析所提出算法的收敛性并给出其收敛条件;最后,通过理论分析表明,收敛速度主要取决于被控对象、控制律增益、修正指数和学习区间的大小.在相同仿真条件下,与传统算法相比,所提出算法具有更快的收敛速度.     

基于扩展收敛域CORDIC的指数变换器设计

在数字信号的研究中,针对提高精度和速度,传统方法不能达到要求.应用指数变换器效果更好.采用CORDIC设计的指数变换器可提高运算效率,易于工程实现.为了降低硬件实现的复杂度和系统资源的消耗,研究了一种基于扩展收敛域CORDIC的指数变换器的设计方案.通过修改迭代序列,有效扩展了指数变换器的收敛域范围,免去了传统CORDIC中复杂的前处理和后处理过程,具有较强的工程实用价值.通过在Matlab上进行仿真和Modelsim硬件描述语言仿真,仿真结果表明扩展收敛域后指数函数的收敛域范围得到了有效的扩大,系统的资源消耗低,验证了该方案的可行性.     

一种含软判决引导的超指数迭代盲均衡算法

超指数迭代盲均衡算法收敛速度快,但对于相位较为敏感,易造成算法的稳态均方误差大.针对这一不足,利用软判决引导思想对修正的超指数迭代盲均衡算法进行并行处理,在保持算法具有较快收敛速度的同时降低了稳态均方误差.计算机仿真结果验证了所提算法的有效性.     

一种提高Vicsek模型收敛效率的新方法

针对Vicsek模型收敛速度较慢和一致程度较低的问题,利用动态网络的拓扑结构并结合复杂网络中度的概念,提出一种以度为权重提高Viesek模型收敛效率的新方法.进一步以动态网络的度的幂指数得到模型的推广形式,该指数的范围为[0,∞).仿真实验结果表明,改进后模型的收敛效率优于原模型,且收敛效率随着指数的增加而增大.     

几种Bussgang族盲均衡算法收敛性能仿真研究

盲均衡是数字通信中的热点问题。收敛速度和剩余均方误差是衡量盲均衡算法性能优劣的重要指标。常数模算法是目前流行的盲均衡算法,有很多优点,但在时变多径衰落无线信道均衡中,该算法存在着收敛速度过慢和剩余均方误差较大的问题。该文研究了三种Bussgang族盲均衡算法,即常数摸算法、归一化常数模算法和超指数迭代算法,在无线信道均衡中的应用,并通过计算机仿真对其性能进行了分析比较。仿真结果表明,在算法剩余均方误差一致的情况下,超指数迭代算法收敛速度最快,归一化常数模算法次之,常数模算法最慢。因此,超指数迭代算法的性能优于归一化常数模算法和传统的常数模算法。该研究结果在工程实践中具有一定的指导意义与应用价值。     

二阶神经网络的全局指数稳定性分析

当神经网络应用于最优化计算时,理想的情形是只有一个全局渐近稳定的平衡点,并且以指数速度趋近于平衡点,从而减少神经网络所需计算时间,二阶神经网络较一般神经网络具有更快的收敛速度,对于二阶连续型Hopfield神经网络,用Lyapunov方法讨论平衡点的全局指数稳定性,给出了平衡点全局指数稳定的几个判别准则,作为特例,获得了连续型Hopfield神经网络全局指数稳定的新判据。     

一种多种群遗传策略的OFDM功率分配算法

针对标准遗传算法在解决比例速率约束下多用户OFDM系统的功率分配出现的收敛速度慢和早熟收敛问题,提出了一种基于多种群遗传策略的功率分配算法。提出算法以业务公平指数为适应度值和以最优个体保持代数为算法终止依据。各个种群使用不同的控制参数,通过移民算子相互联系。仿真结果表明,提出的算法的收敛速度（100代左右）比标准遗传算法的收敛速度（300代左右）快且收敛结果稳定（都基本趋于0）,在最大化总容量的同时很好地维持了用户比例速率公平性。     

Estimate of exponential convergence rate and exponential stabilityfor neural networks

Estimates of exponential convergence rate and exponential stability are studied for a class of neural networks which includes Hopfield neural networks and cellular neural networks. Both local and global exponential convergence are discussed. Theorems for estimation of the exponential convergence rate are established and the bounds on the rate of convergence are given. The domains of attraction in the case of local exponential convergence are obtained. Simple conditions are presented for checking exponential stability of the neural networks.     

Exponential stability of impulsive high-order Hopfield-type neural networks with time-varying delays

This paper considers the problems of global exponential stability and exponential convergence rate for impulsive high-order Hopfield-type neural networks with time-varying delays. By using the method of Lyapunov functions, some sufficient conditions for ensuring global exponential stability of these networks are derived, and the estimated exponential convergence rate is also obtained. As an illustration, an numerical example is worked out using the results obtained.     

On exponential convergence and robustness of least-squares identification

Robustness and convergence properties of exponentially weighted least-squares identification are studied. It is shown that exponential convergence in the noiseless case can be obtained for a class of increasing or decreasing regression vectors. The rate of change of the limits in the regressors affect the convergence rates, which are explicitly given. It is demonstrated that for a sub-class of regressors decreasing-in-the-norm exponential convergence without the noise does not guarantee robustness subject to a bounded noise. Instead, exponential divergence of the estimate is shown in a specific case.     

Global exponential convergence of periodic neural networks with time-varying delays

In this paper, the global exponential convergence of a general class of periodic neural networks with time-varying delays is investigated. Based on the theory of mixed monotone operator, a testable algebraic criteria for ascertaining global exponential convergence is derived. Furthermore, the rate of exponential convergence and bound of the networks are also estimated. Finally, a numerical example is given to show the effectiveness of the obtained results.     

On the convergence rate of ordinal comparisons of random variables

The asymptotic exponential convergence rate of ordinal comparisons follows from well-known results in large deviations theory, where the critical condition is the existence of a finite moment generating function. In this note, we show that this is both a necessary and sufficient condition, and also show how one can recover the exponential convergence rate in cases where the moment generating function is not finite. In particular, by working with appropriately truncated versions of the original random variables, the exponential convergence rate can be recovered     

On the almost sure and mean-square exponential convergence of somestochastic observers

It is pointed out that linear observers used for estimating the state of the discrete-time stochastic-parameter systems are both almost surely and mean-square (MS) exponentially convergent under the same conditions guaranteeing mean-square convergence. In addition to the mean-square convergence properties of linear observers constructed for mean-square stable stochastic-parameter systems, they also possess an almost-sure exponential convergence property, and the rate of MS convergence is exponential. This rate depends on the parameters used in the design     

Stability Analysis and Design of Impulsive Control Systems With Time Delay

A class of impulsive control systems with time-varying delays is considered. By establishing an impulsive delay differential inequality, we analyze the global exponential stability of the impulsive delay systems and estimate the exponential convergence rate. On the basis of the analysis, a design procedure of impulsive controller is presented. The designed impulsive controller not only can globally exponentially stabilize the time delay systems, but also can control the exponential convergence rate of the systems. Two numerical examples are given to illustrate the effectiveness of the method.     

A complete proof of global exponential convergence of a neuralnetwork for quadratic optimization with bound constraints

Sudharsanan and Sundareshan developed (1991) a neural-network model for bound constrained quadratic minimization and proved the global exponential convergence of their proposed neural network. The global exponential convergence is a critical property of the synthesized neural network for solving the optimization problem successfully. However, Davis and Pattison (1992) presented a counterexample to show that the proof given by Sudharsanan and Sundareshan for the global exponential convergence of the neural network is not correct. Bouzerdoum and Pattison (ibid., vol.4, no.2, p.293-303, 1993) then generalized the neural-network model given by Sudharsanan and Sundareshan and derived the global exponential convergence of the neural network under an appropriate condition. In this letter, we demonstrate through an example that the global exponential convergence condition given by Bouzerdoum and Pattison is not always satisfied by the quadratic minimization problem and show that the neural-network model under the global exponential convergence condition given by Bouzerdoum and Pattison is essentially restricted to contractive networks. Subsequently, a complete proof of the global exponential convergence of the neural-network models proposed by Sudharsanan and Sundareshan and Bouzerdoum and Pattison is given for the general case, without resorting to the global exponential convergence condition given by Bouzerdoum and Pattison. An illustrative simulation example is also presented.     

具有马氏跳跃参数的切削加工系统控制问题研究

以单刀具垂直切削加工系统为研究对象, 引入了变进给量控制方法, 建立了具有Markov跳跃参数的时变时滞跳跃系统模型. 通过对系统的随机稳定性分析, 给出了使系统呈均方意义下指数稳定的充分条件, 同时研究了在系统参数矩阵和状态转移率非精确可知情形下的鲁棒稳定性条件, 并讨论了时变时滞参数对系统状态变量指数衰减速率的影响关系. 最后以仿真算例说明了本文所提方案的有效性.