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1.
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 co… 相似文献
2.
Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks 总被引：1，自引：0，他引：1
ZHANG Jian-hai ZHANG Sen-lin LIU Mei-qin 《浙江大学学报(A卷英文版)》2007,8(12):1912-1920
The robust exponential stability of a larger class of discrete-time recurrent neural networks (RNNs) is explored in this paper. A novel neural network model, named standard neural network model (SNNM), is introduced to provide a general framework for stability analysis of RNNs. Most of the existing RNNs can be transformed into SNNMs to be analyzed in a unified way. Applying Lyapunov stability theory method and S-Procedure technique, two useful criteria of robust exponential stability for the discrete-time SNNMs are derived. The conditions presented are formulated as linear matrix inequalities (LMIs) to be easily solved using existing efficient convex optimization techniques. An example is presented to demonstrate the transformation procedure and the effectiveness of the results. 相似文献
3.
A new neural network model termed ＇standard neural network model＇ （SNNM） is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities （LMIs）, which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks （including the chaotic neural network） and nonlinear systems modeled by neural networks or Takagi and Sugeno （T-S） fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper. 相似文献
4.
This paper investigates the exponential synchronization problem of some chaotic delayed neural networks based on the proposed general neural network model, which is the interconnection of a linear delayed dynamic system and a bounded static nonlinear operator, and covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks （CNNs）, bidirectional associative memory （BAM） networks, recurrent multilayer perceptrons （RMLPs）. By virtue of Lyapunov- Krasovskii stability theory and linear matrix inequality （LMI） technique, some exponential synchronization criteria are derived. Using the drive-response concept, hybrid feedback controllers are designed to synchronize two identical chaotic neural networks based on those synchronization criteria. Finally, detailed comparisons with existing results are made and numerical simulations are carried out to demonstrate the effectiveness of the established synchronization laws. 相似文献
5.
通过构造李雅普诺夫函数,利用M-矩阵理论以及Young不等式技巧,给出了一类含有分布时滞和脉冲双向联想记忆神经网络的平衡点的全局指数稳定性的充分条件,这些条件去掉了对激活函数的有界性、单调性和可微性的要求,且在某些情况下更易验证. 相似文献
6.
WU Xiao-juan ZHU Xin-jian CAO Guang-yi TU Heng-yong 《浙江大学学报(A卷英文版)》2007,8(9):1505-1509
The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model. 相似文献
7.
LI Chuandong LIAO Xiaofeng College of Mathematics Physics Chongqing University Chongqing P.R. China Department of Computer Science Engineering Chongqing University Chongqing P.R. China 《重庆大学学报(英文版)》2004,3(1):39-42
1.IntroductionConsideradelayedneural-networkmodeldescribedbythefollowingfunctionaldifferentialequations.()()()()()()11,nniiiijjjijjjijjtautwgutvgutImt===-++-+邋&1,2,,.in=K(1a)or()()()()()()ttttt=-++-+uAuWGuVGuI&,(1b)where()()()()T12,,,ntututut=轾臌uListhestatevectoroftheneuralnetwork;()12diag,,,naaa=AKisadiagonalmatrixwithpositiveentries,i.e.,0ia>;()ijnnw=Wand()ijnnv=Varetheconnectionweightmatrixanddelayedconnectionweightmatrix,re-spectively;()()()()()()()()T1122,,,nntgutgutgut轾=臌GuKde… 相似文献
8.
研究了一类神经网络，包括Hopfield神经网络和细胞神经网络，从神经网络的互连矩阵以及神经元输入输出的激励函数出发，建立了全局情况下的指数收敛速率的估计和指数稳定性的结论。 相似文献
9.
本文讨论了一类具有变时滞模糊FBAM（fuzzybi—directional associate memory）神经网络平衡点的存在性和全局指数稳定性,利用不动点定理及Halanay型不等式获得了平衡点存在唯一性及全局指数稳定性的充分条件,所得结论容易验证．最后举例说明所得结果的可行性。 相似文献
10.
罗安娜 《湖南科技学院学报》2005,26(5):31-34
本文利用巴拿赫不动点定理,得到了BAM神经网络存在唯一概周期解的充分条件。该结果改进了文【9】的结果,并有助于设计具有概周期振动解的BAM神经网络。 相似文献
11.
主要考察了一类动态系统——带有不同时间尺度竞争神经网络的全局指数稳定性问题。通过对此类神经网络模型的建立及分析,应用M矩阵理论和Lyapunov函数法,确定了带有不同时间尺度的竞争神经网络的平衡点的存在性和唯一性,并给出了此类神经网络全局指数稳定的判据。 相似文献
12.
13.
基于Lyapunov方法和不等式分析技巧,讨论了一类时延细胞神经网络(DCNN)全局渐近稳定性问题,给出了一个新的充分判据,该判据可用于设计出全局稳定的神经网络. 相似文献
14.
刘欣萍 《咸阳师范学院学报》2014,(2):1-4
研究了一类变时滞脉冲神经网络的周期解的全局指数稳定性。通过构造恰当的Ly—apunov泛函,并利用线性矩阵不等式方法,得到了确保此网络全局指数稳定的充分条件。数值模拟验证了所得结论的正确性并说明了所给条件的易检验性。 相似文献
15.
This paper studies some special networks structured with serf-organized and driven behavior that coexist in a cluster, moreover, the clusters have dominant intra-cluster and inter-cluster couplings. It is called mixed-system （M-S） here. For this study linear stability analysis was used, and stability conditions for the synchronized state were determined. For the coupling function g（x）, the stability state of the network was discussed in two different cases： the linear case with g（x） = x and the nonlinear case with g（x） = f（x）. Furthermore, the condition for the emergence of chaos in the networks was given. 相似文献
16.
1 Introduction Direct methanol fuel cell ( DMFC) is desirable toserve as the power systemfor portable devices such ascellular phones , portable computers ,etc. due to thetheoretically high energy density and the liquid fuelused that can be stored and tran… 相似文献
17.
高潮 《重庆大学学报(英文版)》2003,2(1)
~~1()((,)(,))((,)(,))entssssssttttte==--Vxxxxfcycy Similar to the discussion of Theorem 1, it is easy to obtain 1(,)(,)entiiittKe-=-?xxcycy for any t0, where K1 is a constant. One can easily follow from formula above that ()()()etttKet---?xxcycy There exists a positive integer m such that ()e1mKqewt--?. A Poincare mapping F: CC by Fc=xw(c) is defined. Then, from Eq.(14), it can be derived that mmq-?FFcycy This implies that Fm is a contraction mapping; hence there exists a unique fixed p… 相似文献
18.
Interval standard neural network models for nonlinear systems 总被引：1，自引：0，他引：1
LIU Mei-qin 《浙江大学学报(A卷英文版)》2006,7(4):530-538
INTRODUCTION Neural networks have been successfully em- ployed for controlling nonlinear systems since the 1990’s (Narendra and Parthasarathy 1990; Hunt et al., 1992; Suykens et al., 1996). In these nonlinear control systems, neural networks have been used either for modelling the system to be controlled, or for design- ing a controller, or both. Recently, the robustness issue has been an important focus of research in neuro-control circles (Suykens et al., 1996; Wams et al., 1999; Aya… 相似文献
19.
Calculation method of ship collision force on bridge using artificial neural network 总被引：1，自引：0，他引：1
Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method （FEM） which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network （ANN）. The radial basis function neural network （RBFNN） employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation effi- ciency in application using MATLAB software. 相似文献
20.
FENG Yan CHEN Yi-min School of Computer Engineering Science Shanghai University Shanghai P.R. China 《上海大学学报(英文版)》2006,(5)
1IntroductionAugmented reality(AR)is a newtechnique based onvirtual reality,which has attracted much attention inrecent years.AR is used to describe a system thatenhances the real world by superi mposing computer-generated information on top of it.It supp… 相似文献