Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays

In this paper, a synchronization problem is investigated for an array of coupled complex discrete-time networks with the simultaneous presence of both the discrete and distributed time delays. The complex networks addressed which include neural and social networks as special cases are quite general. Rather than the commonly used Lipschitz-type function, a more general sector-like nonlinear function is employed to describe the nonlinearities existing in the network. The distributed infinite time delays in the discrete-time domain are first defined. By utilizing a novel Lyapunov–Krasovskii functional and the Kronecker product, it is shown that the addressed discrete-time complex network with distributed delays is synchronized if certain linear matrix inequalities (LMIs) are feasible. The state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that, for all admissible discrete and distributed delays, the dynamics of the estimation error is guaranteed to be globally asymptotically stable. Again, an LMI approach is developed for the state estimation problem. Two simulation examples are provided to show the usefulness of the proposed global synchronization and state estimation conditions. It is worth pointing out that our main results are valid even if the nominal subsystems within the network are unstable.      

Exponential Periodicity of Continuous-time and Discrete-Time Neural Networks with Delays

Exponential periodicity of continuous-time neural networks with delays is investigated. Without assuming the boundedness and differentiability of the activation functions, some new sufficient conditions ensuring existence and uniqueness of periodic solution for a general class of neural systems are obtained. Discrete-time analogue of the continuous-time system with periodic input is formulated and we study their dynamical characteristics. The exponential periodicity of the continuous-time system is preserved by the discrete-time analogue without any restriction imposed on the uniform discretization step-size.     

State Estimation for Markovian Coupled Neural Networks with Multiple Time Delays Via Event-Triggered Mechanism

Neural Processing Letters - This paper focuses on the state estimation problem for a type of coupled neural networks with multiple time delays and markovian jumping communication topologies. To...     

Passivity Analysis of Non-autonomous Discrete-Time Inertial Neural Networks with Time-Varying Delays

Neural Processing Letters - This paper addresses the passivity problem for delayed non-autonomous discrete-time inertial neural networks (NDINN), including the discrete-time switched inertial...     

Robustly Exponential Stability Analysis for Discrete-Time Stochastic Neural Networks with Interval Time-Varying Delays

This paper deals with the problems of the global exponential stability and stabilization for a class of uncertain discrete-time stochastic neural networks with interval time-varying delay. By using the linear matrix inequality method and the free-weighting matrix technique, we construct a new Lyapunov–Krasovskii functional and establish new sufficient conditions to guarantee that the uncertain discrete-time stochastic neural networks with interval time-varying delay are globally exponential stable in the mean square. Furthermore, we extend our consideration to the stabilization problem for a class of discrete-time stochastic neural networks. Based on the state feedback control law, some novel delay-dependent criteria of the robust exponential stabilization for a class of discrete-time stochastic neural networks with interval time-varying delay are established. The controller gains are designed to ensure the global robust exponential stability of the closed-loop systems. Finally, numerical examples illustrate the effectiveness of the theoretical results we have obtained.     

State Estimation for Coupled Uncertain Stochastic Networks With Missing Measurements and Time-Varying Delays: The Discrete-Time Case

This paper is concerned with the problem of state estimation for a class of discrete-time coupled uncertain stochastic complex networks with missing measurements and time-varying delay. The parameter uncertainties are assumed to be norm-bounded and enter into both the network state and the network output. The stochastic Brownian motions affect not only the coupling term of the network but also the overall network dynamics. The nonlinear terms that satisfy the usual Lipschitz conditions exist in both the state and measurement equations. Through available output measurements described by a binary switching sequence that obeys a conditional probability distribution, we aim to design a state estimator to estimate the network states such that, for all admissible parameter uncertainties and time-varying delays, the dynamics of the estimation error is guaranteed to be globally exponentially stable in the mean square. By employing the Lyapunov functional method combined with the stochastic analysis approach, several delay-dependent criteria are established that ensure the existence of the desired estimator gains, and then the explicit expression of such estimator gains is characterized in terms of the solution to certain linear matrix inequalities (LMIs). Two numerical examples are exploited to illustrate the effectiveness of the proposed estimator design schemes.     

Robust Exponential Stability for Discrete-Time Quaternion-Valued Neural Networks with Time Delays and Parameter Uncertainties

Neural Processing Letters - In this paper, the robust exponential stability for discrete-time quaternion-valued neural networks with time delays and parameter uncertainties is investigated. By...     

Finite Time State Estimation of Complex-valued BAM Neutral-type Neural Networks with Time-varying Delays

International Journal of Control, Automation and Systems - This paper considers the finite time state estimation problem of complex-valued bidirectional associative memory (BAM) neutral-type neural...     

含离散和分布时变时延神经网络系统的指数稳定性

针对一类含有离散和分布时延神经网络,在神经激活函数较弱的约束条件下,通过定义一个更具一般性的Lyapunov泛函,使用凸组合技术,得到了新的基于线性矩阵不等式表示的指数稳定性判据.与现有结果相比,这些判据具有较小的保守性.仿真算例表明,得到的结果是有效的且保守性小.     

时变时滞离散递归神经网络系统的状态估计器设计

研究时滞离散递归神经系统的状态估计问题.通过网络输出对神经元的状态进行估计.在较弱的激活函数假设下,通过构造一个新的Lyapunov泛函,引入一个自由权矩阵,并结合Jensen不等式得到了确保误差系统全局指数稳定的充分条件.所得条件依赖于时变时滞的上界和下界,并以线性矩阵不等式的形式给出.最后的数值算例表明了所提出方法的有效性.     

Finite-Time Synchronization of Complex-Valued Neural Networks with Multiple Time-Varying Delays and Infinite Distributed Delays

Neural Processing Letters - This paper investigates finite-time synchronization of complexed-valued neural networks with multiple time-varying delays and infinite distributed delays. By separating...     

State Estimation of Quaternion-Valued Neural Networks with Leakage Time Delay and Mixed Two Additive Time-Varying Delays

In this paper, the state estimation of quaternion-valued neural networks (QVNNs) with leakage time delay, both discrete and distributed two additive time-varying delays is studied. By considering the QVNNs as a whole, instead of decomposing it into two complex-valued neural networks or four real-valued neural networks. Via constructing suitable Lyapunov–Krasovskii functionals, combining free weight matrix, reciprocally convex approach, and matrix inequalities, the sufficient criteria for time delays are given in the form of quaternion-valued linear matrix inequalities and complex-valued linear matrix inequalities. Some observable output measurements are used to estimate the state of neurons, which ensures the global asymptotic stability of the error-state system. Finally, the effectiveness of theoretical analysis is illustrated by a numerical simulation.

     

Global Exponential Stability for Impulsive BAM Neural Networks with Distributed Delays on Time Scales

In this paper, by utilizing the time scale calculus theory, topological degree theory and Hölder’s inequality on time scales, we analyze a class of impulsive BAM neural networks with distributed delays on time scales. Some sufficient conditions are obtained to ensure the existence, uniqueness and the global exponential stability of the equilibrium point. Finally, an example is provided to demonstrate the effectiveness of the results.     

Upper Bounds on Peaks in Discrete-Time Linear Systems

Trajectories of stable linear systems with nonzero initial conditions are known to deviate considerably from the zero equilibrium point at finite time instances. In the paper we analyze transients in discrete-time linear systems and provide upper bounds on deviations (peaks) via use of linear matrix inequalities. An approach to peak-minimizing feedback design is also proposed. An analysis of peak effects for norms of powers of Schur stable matrices is presented and a robust version of the problem is considered. The theory is illustrated by numerical examples.     

On p-th Moment Exponential Stability for Stochastic Cellular Neural Networks with Distributed Delays

International Journal of Control, Automation and Systems - In this paper, a class of stochastic cellular neural networks with distributed delays are investigated. With the help of the method of...     

Upper and Lower Bounds for Selection on the Mesh

A distance-optimal algorithm for selection on the mesh has proved to be elusive, although distance-optimal algorithms for the related problems of routing and sorting have recently been discovered. In this paper we explain, using the notion of adaptiveness, why techniques used in the currently best selection algorithms cannot lead to a distance-optimal algorithm. For worst-case inputs we apply new techniques to improve the previous best upper bound of 1.22n of Kaklamanis et al. [7] to 1.15n . This improvement is obtained in part by increasing the adaptiveness of previous algorithms. Received May 25, 1995; revised June 1, 1996.     

Lower Bounds for Merging Networks

A lower bound theorem is established for the number of comparators in a merging network. Let M(m, n) be the least number of comparators required in the (m, n)-merging networks, and let C(m, n) be the number of comparators in Batcher's (m, n)-merging network, respectively. We prove for n≥1 that M(4, n)=C(4, n) for n≡0, 1, 3 mod 4, M(4, n)≥C(4, n)−1 for n≡2 mod 4, and M(5, n)=C(5, n) for n≡0, 1, 5 mod 8. Furthermore Batcher's (6, 8k+6)-, (7, 8k+7)-, and (8, 8k+8)-merging networks are optimal for k≥0. Our lower bound for (m, n)-merging networks, m≤n, has the same terms as C(m, n) has as far as n is concerned. Thus Batcher's (m, n)-merging network is optimal up to a constant number of comparators, where the constant depends only on m. An open problem posed by Yao and Yao (Lower bounds on merging networks, J. Assoc. Comput. Mach.23, 566–571) is solved: lim_n→∞M(m, n)/n=log m/2+m/2^{log m}.     

Exponential Stability on Stochastic Neural Networks With Discrete Interval and Distributed Delays

This brief addresses the stability analysis problem for stochastic neural networks (SNNs) with discrete interval and distributed time-varying delays. The interval time-varying delay is assumed to satisfy $0≪d_{1}leq d(t) leq d_{2}$ and is described as $d(t)= d_{1}+h(t)$ with $0leq h(t) leq d_{2}-d_{1}$. Based on the idea of partitioning the lower bound $d_{1}$, new delay-dependent stability criteria are presented by constructing a novel Lyapunov–Krasovskii functional, which can guarantee the new stability conditions to be less conservative than those in the literature. The obtained results are formulated in the form of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results.