Recursive Asymptotic Hybrid Matrix Method for Acoustic Waves in Multilayered Piezoelectric Media

This paper presents the recursive asymptotic hybrid matrix method for acoustic waves in multilayered piezoelectric media. The hybrid matrix method preserves the numerical stability and accuracy across large and small thicknesses. For discussion and comparison, the scattering matrix method is also presented in physics-based form and coherent form. The latter form resembles closely that of hybrid matrix method and helps to highlight their relationship and distinction. For both scattering and hybrid matrix methods, their formulations in terms of eigenwaves solution are provided concisely. Making use of the hybrid matrix, the recursive asymptotic method without eigenwaves solution is described and discussed. The method bypasses the intricacies of eigenvalue-eigenvector approach and requires only elementary matrix operations along with thin- layer asymptotic approximation. It can be used to determine Green’s function matrix readily and facilitates the trade-off between computation efficiency and accuracy.

Asymptotical mean square stability of cellular neural networks with random delay

In this paper,the asymptotical mean-square stability analysis problem is considered for a class of cellular neural networks (CNNs) with random delay. Compared with the previous work,the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed cellular neural network is asymptotic mean-square stability. By using some stochastic analysis techniques and Lyapunov-Krasovskii functional,some conditions are derived to ensure that the cellular neural networks with random delay is asymptotical mean-square stability. A numerical example is exploited to show the vadlidness of the established results.

LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks

Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is ad- vanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These conditions were formulated as easily verifiable linear matrix inequalities (LMIs), whose conservativeness is relatively low. The approach proposed extends the known stability results, and can also be applied to other forms of recurrent neural networks (RNNs).

New Results of Global Asymptotical Stability for Impulsive Hopfield Neural Networks with Leakage Time-Varying Delay

In this paper, Hopfield neural networks with impulse and leakage time-varying delay are considered. New sufficient conditions for global asymptotical stability of the equilibrium point are derived by using Lyapunov-Kravsovskii functional, model transformation and some analysis techniques. The criterion of stability depends on the impulse and the bounds of the leakage time-varying delay and its derivative, and is presented in terms of a linear matrix inequality (LMI).

Energy Spectrum for a Short-Range 1/r Singular Potential with a Non-Orbital Barrier Using the Asymptotic Iteration Method

Using the asymptotic iteration method, we obtain the S-wave solution for a short-range three-parameter central potential with 1/r singularity and with a non-orbital barrier. To the best of our knowledge, this is the first attempt at calculating the energy spectrum for this potential, which was introduced by H. Bahlouli and A. D. Alhaidari and for which they obtained the “potential parameter spectrum”. Our results are also independently verified using a direct method of diagonalizing the Hamiltonian matrix in the J-matrix basis.

New Delay-dependent Global Asymptotic Stability Condition for Hopfield Neural Networks with Time-varying Delays

This paper deals with the global asymptotic stability problem for Hopfield neural networks with time-varying delays. By resorting to the integral inequality and constructing a Lyapunov-Krasovskii functional, a novel delay-dependent condition is established to guarantee the existence and global asymptotic stability of the unique equilibrium point for a given delayed Hopfield neural network. This criterion is expressed in terms of linear matrix inequalities (LMIs), which can be easily checked by utilizing the recently developed algorithms for solving LMIs. Examples are provided to demonstrate the effectiveness and reduced conservatism of the proposed condition.

Robust asymptotic stability for BAM neural networks with time-varying delays via LMI approach

Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality(LMI) conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.

船舶动力定位非线性模型预测控制

针对受船舶运动状态和推力饱和约束影响的动力定位控制问题,本文提出了一种非线性模型预测控制方法。依据多面体描述理论建立了船舶动力定位多面体描述偏差模型,针对上述模型采用线性矩阵不等式设计非线性模型预测控制器中的终端要素,并证明了闭环系统的稳定性。仿真结果表明:设计的控制器能在满足约束条件和受环境干扰条件下快速控制船舶稳定在计划航向和计划船位,验证了控制器的有效性和本质鲁棒性。

Globally robust nonlinear PID controllers for robot manipulators with an uncertain Jacobian matrix

Based on a continuous piecewise-differentiable increasing functions vector, a class of robust nonlinear PID(RN-PID) controllers is proposed for setpoint control with uncertain Jacobian matrix. Globally asymptotic stability is guaranteed and only position and joint velocity measurements are required. And stability problem arising from integral action and integrator windup, are consequendy resolved. Furthermore, RN-PID controllers can be of effective alternative for anti-integrator-wind-up,the control performance would not be very bad in the presence of rough parameter tuning.

On stabilization for a class of nonlinear stochastic time-delay systems:a matrix inequality approach

这篇论文与状态对待非线性的随机的时间延期系统的反馈稳定；控制依赖者噪音。一些局部地(全球性) 要用体力地， stabilizable 条件以独立于延期尺寸的矩阵不平等被给。当它被用于线性随机的时间延期系统时，为州反馈的稳定的足够的条件经由线性矩阵不平等被介绍。几以前的结果与两个状态被扩大到更一般的系统；控制依赖者噪音，；容易的计算算法也被给。

Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays

Fuzzy cellular neural networks (FCNNs) are special kinds of cellular neural networks (CNNs). Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality (LMI) approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov–Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.

Constructing Confidence Regions for Autoregressive-Model Parameters

We discuss formulas and techniques for finding maximum-likelihood estimators of parameters of autoregressive (with particular emphasis on Markov and Yule) models, computing their asymptotic variance-covariance matrix and displaying the resulting confidence regions;Monte Carlo simulation is then used to establish the accuracy of the corresponding level of confidence. The results indicate that a direct application of the Central Limit Theorem yields errors too large to be acceptable;instead, we recommend using a technique based directly on the natural logarithm of the likelihood function, verifying its substantially higher accuracy. Our study is then extended to the case of estimating only a subset of a model’s parameters, when the remaining ones (called nuisance) are of no interest to us.

基于投切状态矩阵的MMC系统电容电压均衡策略

为了奠定模块化多电平换流器(modular multilevel converter,MMC)自平衡的理论依据,构建了MMC状态空间模型,证明了MMC电容电压的一致渐进稳定性。研究了MMC电容电压非均衡状态下的静态工作点,揭示了投切状态矩阵的秩和MMC电容电压静态工作点之间的关联。基于投切状态矩阵自平衡理论,针对控制频率较低或子模块数量较多时轮换速度慢的问题,提出了一种基于投切状态矩阵分组轮换的电容电压均衡控制策略,能够有效降低电容电压的波动率。最后,在Matlab/Simulink中搭建了MMC电磁暂态仿真模型,验证了所证结论的正确性和所提方法的有效性。

基于媒体报道影响的SEIS传染病模型

综合考虑媒体的数量、报道力度等因素,建立一类受媒体报道影响的SEIS传染病模型.定义模型的基本再生数R 0,基于第二加性复合矩阵法得到疾病平衡点全局渐近稳定的条件.数值模拟结果显示:媒体的存在使得传染病传播达到平衡态的时间增加,且平衡态时的易感者人群密度明显多于无媒体报道下的易感者人群密度;随着媒体报道力度的增加,平衡态时的易感者人群密度也在增大;随着报道媒体数量的增加,染病者人群密度减少,进而有效控制了传染病的传播.

飞行器表面等离子体融合分析法

为对飞行器表面的等离子体态进行实时监测和分析,提出了一种新型的融合神经网络的等离子体分析方法。对电磁波在等离子体中的传播情况进行了分析,使用渐近薄膜近似和初等矩阵运算的方法计算相关参数,再引入神经网络进行多特征的优化,并使用自适应学习率调整。实验结果表明:在数据传输稳定性测试中,该文方法在碰撞频率为0.5 GHz时的数据传输速率在1020 Mbps附近波动;进行净运算时间测试时,在碰撞频率为5 GHz、面积区域为2 m^(2)时的净运算时间为253 ms。

Truncated Estimator of Asymptotic Covariance Matrix in Partially Linear Models with Heteroscedastic Errors

有 heteroscedastic 或连续地相关的错误的一个部分线性的回归模型这里被学习。参量的最少的广场评价(SLSE ) 被需要以便使用半使统计推理成为 asymptoticcovariance 矩阵的一个一致评估者，是众所周知的。当错误是 heteroscedastic 或连续地相关时， asymptotic 协变性矩阵的传统的基于剩余的评估者不是一致的。在这篇论文，我们由截断建议一个新评估者，它是穿怀特衣服的过程的延期。当截断的参数与某率收敛到无穷时，这个评估者被显示一致。

基于随机矩阵理论的频谱感知技术研究综述

认知无线电技术,可以提高频谱资源的利用率,应用前景广阔,极有可能引发无线通信领域的又一次重大变革。但频谱感知作为认知无线电技术的首要环节,其性能还远不能满足实际需要。研究出一种真正能够实际应用的高性能频谱感知方法已经成为一个亟待解决的问题。近年来,与无线通信领域密切相关的新理论——随机矩阵理论(RMT),在频谱感知领域得到了进一步的应用,实现了高性能频谱感知。且RMT最新研究成果——非渐近随机矩阵理论(Non-AsymptoticRMT)有望将这项高性能感知技术真正推向实际应用。因此,基于随机矩阵理论的频谱感知技术具有重要的研究价值。本文总结RMT在认知无线电频谱感知领域的发展历程、介绍其基本原理、归纳其主要特点,并展望进一步研究的方向。

离散模糊系统分析与设计的模糊Lyapunov方法

研究离散T-S模糊控制系统基于模糊Lyapunov函数的稳定性分析及控制器设计问题.首先,构造出离散型模糊Lyapunov函数,模糊Lyapunov函数是系数与T-S模糊系统的模糊规则权重相对应的复合型Lyapunov函数.然后,得到了开环系统新的稳定性充分条件,与公共Lyapunov方法的结果相比,这一条件更为宽松.进而,基于一系列线性矩阵不等式设计出模糊控制器.最后,仿真实例说明了该方法的算法和本文条件的优越性.

Hopfield神经网络系统的全局稳定性分析(英文)

研究一类Hopfield神经网络系统的平衡状态的存在性、唯一性与全局稳定性 ,这类系统放弃了以前对激励函数的有界性、单调性和可微性要求 .利用M矩阵理论 ,通过构造适当的Lyapunov函数 ,得到了系统全局渐近稳定的充分条件 .