This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point,...This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov-Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result.展开更多
This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, som...This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, some sufficient conditions are presented for global exponential stability of delay competitive neural networks with different time scales. These conditions obtained have important leading significance in the designs and applications of global exponential stability for competitive neural networks. Finally, an example with its simulation is provided to demonstrate the usefulness of the proposed criteria.展开更多
In this paper, global exponential stability of almost periodic solution of cellular neural networks with time-varing delays （CNNVDs） is considered. By using the methods of the topological degree theory and generaliz...In this paper, global exponential stability of almost periodic solution of cellular neural networks with time-varing delays （CNNVDs） is considered. By using the methods of the topological degree theory and generalized Halanay inequality, a few new applicable criteria are established for the existence and global exponential stability of almost periodic solution. Some previous results are improved and extended in this letter and one example is given to illustrate the effectiveness of the new results.展开更多
By using the properties of nonnegative matrices and techniques of differential inequalities,some sufficient conditions for the global exponential stability of cellular neural networks with time delays were obtained.Th...By using the properties of nonnegative matrices and techniques of differential inequalities,some sufficient conditions for the global exponential stability of cellular neural networks with time delays were obtained.The criteria do not require such conditions as boundedness and differentiability of activation functions.The conditions of the theorem were verified.展开更多
In this paper, the existence of almost periodic solutions to general BAM neural networks with leakage delays on time scales is first studied, by using the exponential dichotomy method of linear differential equations ...In this paper, the existence of almost periodic solutions to general BAM neural networks with leakage delays on time scales is first studied, by using the exponential dichotomy method of linear differential equations and fixed point theorem. Then, the exponential stability of almost periodic solutions to such BAM neural networks on time scales is discussed by utilizing differential inequality. Finally, an example is given to support our results in this paper and the results are up-to-date.展开更多
This paper investigates the global exponential stability of reaction-diffusion neural networks with discrete and distributed time-varying delays. By constructing a more general type of Lyapunov-Krasovskii functional c...This paper investigates the global exponential stability of reaction-diffusion neural networks with discrete and distributed time-varying delays. By constructing a more general type of Lyapunov-Krasovskii functional combined with a free-weighting matrix approach and analysis techniques, delay-dependent exponential stability criteria are derived in the form of linear matrix inequalities. The obtained results are dependent on the size of the time-vaxying delays and the measure of the space, which are usually less conservative than delay-independent and space-independent ones. These results are easy to check, and improve upon the existing stability results. Some remarks are given to show the advantages of the obtained results over the previous results. A numerical example has been presented to show the usefulness of the derived linear matrix inequality （LMI）-based stability conditions.展开更多
The global exponentially stability and the existence of periodic solutions of a class of Hopfield neural networks with time delays are investigated. By constructing a novel Lyapunov function, new criteria are provided...The global exponentially stability and the existence of periodic solutions of a class of Hopfield neural networks with time delays are investigated. By constructing a novel Lyapunov function, new criteria are provided to guarantee the global exponentially stability of such systems. For the delayed Hopfield neural networks with time-varying external inputs, new criteria are also acquired for the existence and exponentially stability of periodic solutions. The results are generalizations and improvements of some recent achievements reported in the literature on networks with time delays.展开更多
The problem of the global exponential robust stability of interval neural networks with a fixed delay was studied by an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI). Th...The problem of the global exponential robust stability of interval neural networks with a fixed delay was studied by an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI). The results obtained provide an easily verified guideline for determining the exponential robust stability of delayed neural networks. The theoretical analysis and numerical simulations show that the results are less conservative and less restrictive than those reported recently in the literature.展开更多
In this work, the stability issues of the equilibrium points of the cellular neural networks with multiple time delays and impulsive effects are investigated. Based on the stability theory of Lyapunov-Krasovskii, the ...In this work, the stability issues of the equilibrium points of the cellular neural networks with multiple time delays and impulsive effects are investigated. Based on the stability theory of Lyapunov-Krasovskii, the method of linear matrix inequality （LMI） and parametrized first-order model transformation, several novel conditions guaranteeing the delaydependent and the delay-independent exponential stabilities are obtained. A numerical example is given to illustrate the effectiveness of our results.展开更多
As a useful alternative of Shewhart control chart, exponentially weighted moving average （EWMA） control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was...As a useful alternative of Shewhart control chart, exponentially weighted moving average （EWMA） control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA （MEWMA） control charts, in which the optimal parameter pair （ λ, k） or （ λ, h ） was searched by using the generalized regression neural network （GRNN）. The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length （ARLo） and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported.展开更多
The exponential stability of the delayed cellular neural networks （DCNN＇s） is investigated. By dividing the network state variables into some parts according to the characters of the neural networks, some new suffi...The exponential stability of the delayed cellular neural networks （DCNN＇s） is investigated. By dividing the network state variables into some parts according to the characters of the neural networks, some new sufficient conditions of exponential stability are derived via constructing a Liapunov function. It is shown that the conditions differ from previous ones. The new conditions, which are associated with some initial value, are represented by some blocks of the interconnection matrix.展开更多
The robust global exponential stability of a class of interval recurrent neural networks（RNNs） is studied,and a new robust stability criterion is obtained in the form of linear matrix inequality.The problem of robus...The robust global exponential stability of a class of interval recurrent neural networks（RNNs） is studied,and a new robust stability criterion is obtained in the form of linear matrix inequality.The problem of robust stability of interval RNNs is transformed into a problem of solving a class of linear matrix inequalities.Thus,the robust stability of interval RNNs can be analyzed by directly using the linear matrix inequalities（LMI） toolbox of MATLAB.Numerical example is given to show the effectiveness of the obtained results.展开更多
This paper derives some sufficient conditions for exponential stability for the equilibrium point by dividing the state variables of the system according to the characters of the neural networks. The new conditions ar...This paper derives some sufficient conditions for exponential stability for the equilibrium point by dividing the state variables of the system according to the characters of the neural networks. The new conditions are described by some blocks of the interconnection matrix. An example is given to demonstrate the effectiveness of the proposed theory.展开更多
In this paper, we investigate the dynamics and the global exponential stability of a new class of Hopfield neural network with time-varying and distributed delays. In fact, the properties of norms and the contraction ...In this paper, we investigate the dynamics and the global exponential stability of a new class of Hopfield neural network with time-varying and distributed delays. In fact, the properties of norms and the contraction principle are adjusted to ensure the existence as well as the uniqueness of the pseudo almost periodic solution, which is also its derivative pseudo almost periodic. This results are without resorting to the theory of exponential dichotomy. Furthermore, by employing the suitable Lyapunov function, some delayindependent sufficient conditions are derived for exponential convergence. The main originality lies in the fact that spaces considered in this paper generalize the notion of periodicity and almost periodicity. Lastly, two examples are given to demonstrate the validity of the proposed theoretical results.展开更多
We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-...We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-time switching rule,as a more general switching rule, is adopted to describe this variation characteristic. Afterwards, based on the classical Lyapunov stability theory, a Lyapunov function is established, in which the information about the Markov jump feature of the system mode and the persistent dwell-time switching of the transition probabilities is considered simultaneously.Furthermore, via using the stochastic analysis method and some advanced matrix transformation techniques, some sufficient conditions are obtained such that the estimation error system is mean-square exponentially stable with an H_(∞) performance level, from which the specific form of the estimator can be obtained. Finally, the rationality and effectiveness of the obtained results are verified by a numerical example.展开更多
The paper is devoted to periodic attractor of delayed Hopfield neural networks with time-varying. By constructing Lyapunov functionals and using inequality techniques, some new sufficient criteria are obtained to guar...The paper is devoted to periodic attractor of delayed Hopfield neural networks with time-varying. By constructing Lyapunov functionals and using inequality techniques, some new sufficient criteria are obtained to guarantee the existence and global exponential stability of periodic attractor. Our results improve and extend some existing ones in [13-14]. One example is also worked out to demonstrate the advantages of our results.展开更多
In this paper, based on new Lyapunov function, the asymptotic properties of the dynamic neural system with asymmetric connection weights are investigated. Since the dynamic neural system with asymmetric connection wei...In this paper, based on new Lyapunov function, the asymptotic properties of the dynamic neural system with asymmetric connection weights are investigated. Since the dynamic neural system with asymmetric connection weights is more general than that with symmetric ones, the new results are significant in both theory and applications. Specially the new result can cover the asymptotic stability results of linear systems as special cases.展开更多
基金Project supported by the National Natural Science Foundations of China(Grant No.70871056)the Society Science Foundation from Ministry of Education of China(Grant No.08JA790057)the Advanced Talents'Foundation and Student's Foundation of Jiangsu University,China(Grant Nos.07JDG054 and 07A075)
文摘This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov-Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result.
基金supported by National Natural Science Foundation of China (Grant No 60674026)the Jiangsu Provincial Natural Science Foundation of China (Grant No BK2007016)Program for Innovative Research Team of Jiangnan University of China
文摘This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, some sufficient conditions are presented for global exponential stability of delay competitive neural networks with different time scales. These conditions obtained have important leading significance in the designs and applications of global exponential stability for competitive neural networks. Finally, an example with its simulation is provided to demonstrate the usefulness of the proposed criteria.
文摘In this paper, global exponential stability of almost periodic solution of cellular neural networks with time-varing delays （CNNVDs） is considered. By using the methods of the topological degree theory and generalized Halanay inequality, a few new applicable criteria are established for the existence and global exponential stability of almost periodic solution. Some previous results are improved and extended in this letter and one example is given to illustrate the effectiveness of the new results.
基金the Foundation of Technology Project of Chongqing Education Commission (No. 041503)
文摘By using the properties of nonnegative matrices and techniques of differential inequalities,some sufficient conditions for the global exponential stability of cellular neural networks with time delays were obtained.The criteria do not require such conditions as boundedness and differentiability of activation functions.The conditions of the theorem were verified.
基金Partially supported by the Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities'Association(Grant No.202101BA070001-045).
文摘In this paper, the existence of almost periodic solutions to general BAM neural networks with leakage delays on time scales is first studied, by using the exponential dichotomy method of linear differential equations and fixed point theorem. Then, the exponential stability of almost periodic solutions to such BAM neural networks on time scales is discussed by utilizing differential inequality. Finally, an example is given to support our results in this paper and the results are up-to-date.
基金supported by the National Natural Science Foundation of China (Grant No. 60974139)partially supported by the Fundamental Research Funds for the Central Universities
文摘This paper investigates the global exponential stability of reaction-diffusion neural networks with discrete and distributed time-varying delays. By constructing a more general type of Lyapunov-Krasovskii functional combined with a free-weighting matrix approach and analysis techniques, delay-dependent exponential stability criteria are derived in the form of linear matrix inequalities. The obtained results are dependent on the size of the time-vaxying delays and the measure of the space, which are usually less conservative than delay-independent and space-independent ones. These results are easy to check, and improve upon the existing stability results. Some remarks are given to show the advantages of the obtained results over the previous results. A numerical example has been presented to show the usefulness of the derived linear matrix inequality （LMI）-based stability conditions.
基金the Science Foundation of Guangdong Province in China
文摘The global exponentially stability and the existence of periodic solutions of a class of Hopfield neural networks with time delays are investigated. By constructing a novel Lyapunov function, new criteria are provided to guarantee the global exponentially stability of such systems. For the delayed Hopfield neural networks with time-varying external inputs, new criteria are also acquired for the existence and exponentially stability of periodic solutions. The results are generalizations and improvements of some recent achievements reported in the literature on networks with time delays.
文摘The problem of the global exponential robust stability of interval neural networks with a fixed delay was studied by an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI). The results obtained provide an easily verified guideline for determining the exponential robust stability of delayed neural networks. The theoretical analysis and numerical simulations show that the results are less conservative and less restrictive than those reported recently in the literature.
基金Project supported by the National Natural Science Foundation of China (Grant Nos 60604007 and 50775226)
文摘In this work, the stability issues of the equilibrium points of the cellular neural networks with multiple time delays and impulsive effects are investigated. Based on the stability theory of Lyapunov-Krasovskii, the method of linear matrix inequality （LMI） and parametrized first-order model transformation, several novel conditions guaranteeing the delaydependent and the delay-independent exponential stabilities are obtained. A numerical example is given to illustrate the effectiveness of our results.
基金Funded by the National Key Technologies R&D Programs of China (No.2002BA105C)
文摘As a useful alternative of Shewhart control chart, exponentially weighted moving average （EWMA） control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA （MEWMA） control charts, in which the optimal parameter pair （ λ, k） or （ λ, h ） was searched by using the generalized regression neural network （GRNN）. The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length （ARLo） and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported.
基金Supported by the National Natural Science Foundation of China (No.90208003, 30200059) and the Science and Technology Research Foundation of Education Ministry of China (No.02065)
文摘The exponential stability of the delayed cellular neural networks （DCNN＇s） is investigated. By dividing the network state variables into some parts according to the characters of the neural networks, some new sufficient conditions of exponential stability are derived via constructing a Liapunov function. It is shown that the conditions differ from previous ones. The new conditions, which are associated with some initial value, are represented by some blocks of the interconnection matrix.
基金Supported by the Natural Science Foundation of Shandong Province (ZR2010FM038,ZR2010FL017)
文摘The robust global exponential stability of a class of interval recurrent neural networks（RNNs） is studied,and a new robust stability criterion is obtained in the form of linear matrix inequality.The problem of robust stability of interval RNNs is transformed into a problem of solving a class of linear matrix inequalities.Thus,the robust stability of interval RNNs can be analyzed by directly using the linear matrix inequalities（LMI） toolbox of MATLAB.Numerical example is given to show the effectiveness of the obtained results.
文摘This paper derives some sufficient conditions for exponential stability for the equilibrium point by dividing the state variables of the system according to the characters of the neural networks. The new conditions are described by some blocks of the interconnection matrix. An example is given to demonstrate the effectiveness of the proposed theory.
文摘In this paper, we investigate the dynamics and the global exponential stability of a new class of Hopfield neural network with time-varying and distributed delays. In fact, the properties of norms and the contraction principle are adjusted to ensure the existence as well as the uniqueness of the pseudo almost periodic solution, which is also its derivative pseudo almost periodic. This results are without resorting to the theory of exponential dichotomy. Furthermore, by employing the suitable Lyapunov function, some delayindependent sufficient conditions are derived for exponential convergence. The main originality lies in the fact that spaces considered in this paper generalize the notion of periodicity and almost periodicity. Lastly, two examples are given to demonstrate the validity of the proposed theoretical results.
基金supported by the National Natural Science Foundation of China (Grant Nos. 61873002, 61703004, 61973199, 61573008, and 61973200)。
文摘We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-time switching rule,as a more general switching rule, is adopted to describe this variation characteristic. Afterwards, based on the classical Lyapunov stability theory, a Lyapunov function is established, in which the information about the Markov jump feature of the system mode and the persistent dwell-time switching of the transition probabilities is considered simultaneously.Furthermore, via using the stochastic analysis method and some advanced matrix transformation techniques, some sufficient conditions are obtained such that the estimation error system is mean-square exponentially stable with an H_(∞) performance level, from which the specific form of the estimator can be obtained. Finally, the rationality and effectiveness of the obtained results are verified by a numerical example.
基金Foundation item： Supported by the National Science Foundation of Hunan Provincial Education Department （06C792 07C700）
文摘The paper is devoted to periodic attractor of delayed Hopfield neural networks with time-varying. By constructing Lyapunov functionals and using inequality techniques, some new sufficient criteria are obtained to guarantee the existence and global exponential stability of periodic attractor. Our results improve and extend some existing ones in [13-14]. One example is also worked out to demonstrate the advantages of our results.
文摘In this paper, based on new Lyapunov function, the asymptotic properties of the dynamic neural system with asymmetric connection weights are investigated. Since the dynamic neural system with asymmetric connection weights is more general than that with symmetric ones, the new results are significant in both theory and applications. Specially the new result can cover the asymptotic stability results of linear systems as special cases.
