时滞Markov跳变BAM神经网络的鲁棒稳定性

针对一类具有Markov跳变参数的时滞双向联想记忆(BAM)神经网络,研究了其在系统参数不确定情况下的鲁棒稳定性.在不要求连接权矩阵的对称性和激励函数的可微性与单调性的情况下,通过构造适当的Lyapunov泛函得到了此类神经网络均方鲁棒稳定的充分条件.该条件考虑了时滞Markov跳变神经网络中参数的不确定性,改进了现有文献中的关于时滞Markov跳变神经网络的稳定性条件,所得结果以线性矩阵不等式(LMI)的形式给出.最后,通过实例仿真验证了所得结论的有效性.     

Stability of discrete Hopfield neural networks with delay

1.INTRODUCTION DiscreteHopfieldneuralnetwork(DHNN)isoneof thefamousneuralnetworkswithawiderangeofap plications,suchascontentaddressablememory,pat ternrecognition,andcombinatorialoptimiza tion[1～5].ThestabilityofDHNNmeansthatevery trajectorymustconvergetowardsanequilibrium point,oralimitcycle.Becausethestabilityof DHNNisthefoundationofthenetwork’sapplica tionsandisthemostbasicandimportantproblem,thestabilityanalysisoftheDHNNhasattractedcon siderableinterest.Manyresearchershavefocu…     

区间系统鲁棒绝对稳定性分析

针对区间型Lurie间接控制系统和直接控制系统,提出了鲁棒绝对稳定性分析的新方法。该方法通过引入区间矩阵的一种新的描述方法,利用李亚普诺夫(Lyapunov)稳定性理论分别给出了间接控制系统和直接控制系统的鲁棒绝对稳定性的新的判别方法。所得判据以线性矩阵不等式的形式进行描述,保守性小,计算方便。分别就间接控制系统和直接控制系统,给出了算例以说明方法的有效性。     

Passivity analysis for uncertain stochastic neural networks with discrete interval and distributed time-varying delays

The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays. The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique, new delay dependent passivity conditions are derived in terms of linear matrix inequalities (LMIs). Finally, numerical examples are given to show the less conservatism of the proposed conditions.     

具有时滞的区间大系统的鲁棒稳定性

应用Lyapunov函数方法讨论了具有时滞的区间大系统的鲁棒稳定性,给出了该系统鲁棒稳定的充分条件,同时还讨论了一类不确定时滞大系统的鲁棒稳定性,最后给出了结果的示例并与前人的结果进行了比较。     

一类Markov跳变神经网络的时滞相关鲁棒稳定性

针对一类Markov跳变神经网络,研究了其在系统参数不确定情况下的全局鲁棒稳定性。利用Leibniz Newton公式对原系统进行等价变换,基于Lyapunov 稳定性理论,并结合Moon不等式得到了此类Markov跳变神经网络时滞相关均方鲁棒稳定性的判别条件。所得结果以线性矩阵不等式(linear matrix inequality, LMI)的形式给出,容易被Matlab中的LMI工具箱验证。最后,通过一个算例验证了所得结论的有效性。     

Output-feedback adaptive stochastic nonlinear stabilization using neural networks

For the first time, an adaptive backstepping neural network control approach is extended to a class of stochastic nonlinear output-feedback systems. Different from the existing results, the nonlinear terms are assumed to be completely unknown and only a neural network is employed to compensate for all unknown nonlinear functions so that the controller design is more simplified. Based on stochastic LaSalle theorem, the resulted closed-loop system is proved to be globally asymptotically stable in probability....     

Robust fuzzy control of Takagi-Sugeno fuzzy neural networks with discontinuous activation functions and time delays

The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks(TSFNN) with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities(LMIs),we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.     

Stability of stochastic neural networks with Markovian jumping parameters

The global asymptotical stability for a class of stochastic delayed neural networks (SDNNs) with new delay-dependent stability conditions are derived. All results are expressed in terms of linear matrix inequality (LMI), and a numerical example is presented to illustrate the correctness and less conservativeness of the proposed method.     

Global exponential stability for delayed cellular neural networks and estimate of exponential convergence rate

Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.     

New criteria on delayed state feedback stabilization for stochastic systems with time-varying delay

The problems of robust exponential stability in mean square and delayed state feedback stabilization for uncertain stochastic systems with time-varying delay are studied.By using Jensen’s integral inequality and combining with the free weighting matrix approach,new delay-dependent stability conditions and delayed state feedback stabilization criteria are obtained in terms of linear matrix inequalities.Meanwhile,the proposed delayed state feedback stabilization criteria are more convenient in application than the existing ones since fewer tuning parameters are involved.Numerical examples are given to illustrate the effectiveness of the proposed methods.     

Registration algorithm for sensor alignment based on stochastic fuzzy neural network

1.INTRODUCTION 1.1　SensorSurveillanceSystem Surveillancesystemisusedinmanycivilianandmili taryapplicationssystems.Sub surveillancesystem contains:air trafficcontrolsystem,airdefencesys temandnavigationdecisionandcontrolsystem,etc. Inordertoperfectlydescribe“themovementofthe target”,thesystemneedtoobtainthetrackofthe target.Thedatafusionofthetargetandtheprocess buildingtheirtracksarecalledasmultipletargettrac ing.Themainfunctionofthetargetandtheprocess buildingtheirtracksarecalledas…     

Robust stability test for 2-D continuous-discrete systems with interval parameters

It is revealed that the dynamic stability of 2-D recursive continuous-discrete systems with interval parameters involves the problem of robust Hurwitz-Schur stability of bivariate polynomials family. It is proved that the Hurwitz-Schur stability of the denominator polynomials of the systems is necessary and sufficient for the asymptotic stability of the 2-D hybrid systems. The 2-D hybrid transformation, i. e. 2-D Laplace-Z transformation, has been proposed to solve the stability analysis of the 2-D continuous-discrete systems, to get the 2-D hybrid transfer functions of the systems. The edge test for the Hurwitz-Schur stability of interval bivariate polynomials is introduced. The Hurwitz-Schur stability of the interval family of 2-D polynomials can be guaranteed by the stability of its finite edge polynomials of the family. An algorithm about the stability test of edge polynomials is given.     

基于神经网络的BTT导弹鲁棒动态逆设计

针对存在不确定性的BTT导弹系统,基于神经网络提出了一种鲁棒动态逆控制系统设计方法。首先应用双时标假设将BTT导弹动力学分离为快变状态动力学和慢变状态动力学。然后,在巧妙地利用导弹气动参数特性设计Lyapunov函数的基础上,对快变状态动力学和慢变状态动力学分别进行动态逆控制设计。设计中应用RBF神经网络来逼近系统中存在的不确定性,证明了闭环系统的所有信号均有界且指数收敛至系统原点的一个邻域。最后给出的BTT导弹非线性六自由度数字仿真结果验证了该算法的有效性。     

动态神经网络的非线性系统鲁棒辨识和状态观测

针对多变量仿射非线性系统辨识和状态估计问题。采用动态神经网络作为辨识器和观测器,从而将系统辨识和状态观测设计过程合二为一,给出了辨识和状态估计的系统设计方法及具体设计步骤。在考虑建模误差项影响的情况下,采用基于Lyapunov理论的权调节律确保权值估计误差和状态观测误差的一致最终有界。仿真结果验证了所提方法的有效性。     

随机需求下公交时刻表设计的鲁棒性优化

考虑在实际运营中乘客需求具有随机性,固定需求下优化的公交时刻表不适应运营的要求.随机需求下的期望值模型忽略了不利可能事件对运营的负面影响,针对此情况研究随机需求下公交时刻表设计的鲁棒性优化.模型综合考虑乘客成本与运营成本,采用鲁棒性优化权衡目标期望值与偏差期望值.结合随机模拟技术,选用遗传算法求解模型.给出了算例,验证了模型和算法的有效性.通过比较固定需求模型、随机需求期望值模型、随机需求鲁棒性模型,说明在鲁棒性优化下需要提供更多的交通供给以降低偏差期望值.最后,对鲁棒性模型中的偏差权重系数进行了灵敏度分析.     

Exponential stability for cellular neural networks: an LMI approach

A new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks （DCNNs） is presented. It is shown that the use of a more general type of Lyapunov-Krasovskii function enables the derivation of new results for an exponential stability of the equilibrium point for DCNNs. The results establish a relation between the delay time and the parameters of the network. The results are also compared with one of the most recent results derived in the literature.     

变时滞广义神经网络的指数稳定性

讨论具有可变时滞的Cohen Grossberg广义神经网络平衡态的稳定性问题。以推广的Halanay微分不等式为工具,通过构造合适的Lyapunov函数,结合Dini导数和不等式技巧,对具有可变时滞(适合于任何时滞有界情况)的广义神经网络平衡态的指数稳定性进行分析。在适当的假设条件下,得到了系统渐近指数稳定的几个充分性判据,与此同时也给出了相应的Lyapunov指数。     

New results on the robust stability analysis of neural networks with discrete and distributed time delays

Delay-dependent robust stability of cellular neural networks with time-varying discrete and distributed time-varying delays is considered. Based on Lyapunov stability theory and the linear matrix inequality （LMIs） technique, delay-dependent stability criteria are derived in terms of LMIs avoiding bounding certain cross terms, which often leads to conservatism. The effectiveness of the proposed stability criteria and the improvement over the existing results are illustrated in the numerical examples.     

时滞细胞神经网络的全局渐近鲁棒稳定性分析

针对一类具有时滞的细胞神经网络系统,利用新的Lyapunov Krasovskii函数,给出了系统全局渐近鲁棒稳定的时滞相关稳定性条件。其结果以线性矩阵不等式的形式给出,可以很容易求出系统稳定的时滞上界。进而可以很容易得到时滞无关稳定性结果,且该结果包含了现有文献中许多关于时滞无关稳定性分析的结果。数值算例说明了结论的有效性。