Global exponential stability in Lagrange sense for neutral type recurrent neural networks

In this paper, the global exponential stability in Lagrange sense for continuous neutral type recurrent neural networks (NRNNs) with multiple time delays is studied. Three different types of activation functions are considered, including general bounded and two types of sigmoid activation functions. By constructing appropriate Lyapunov functions, some easily verifiable criteria for the ultimate boundedness and global exponential attractivity of NRNNs are obtained. These results can be applied to monostable and multistable neural networks as well as chaos control and chaos synchronization.     

Stability analysis of discrete-time switched systems: a switched homogeneous Lyapunov function method

This paper addresses the stability issue of discrete-time switched systems with guaranteed dwell-time. The approach of switched homogeneous Lyapunov function of higher order is formally proposed. By means of this approach, a necessary and sufficient condition is established to check the exponential stability of the considered system. With the observation that switching signal is actually arbitrary if the dwell time is one sample time, a necessary and sufficient condition is also presented to verify the exponential stability of switched systems under arbitrary switching signals. Using the augmented argument, a necessary and sufficient exponential stability criterion is given for discrete-time switched systems with delays. A numerical example is provided to show the advantages of the theoretical results.     

On event design in event-triggered feedback systems

This paper studies event design in event-triggered feedback systems. A novel event-triggering scheme is presented to ensure exponential stability of the resulting sampled-data system. The scheme postpones the triggering of events over previously proposed methods and therefore enlarges the intersampling period. The resulting intersampling periods and deadlines are bounded strictly away from zero when the continuous time system is input-to-state stable with respect to measurement errors.     

Mean square exponential stability of impulsive stochastic reaction-diffusion Cohen–Grossberg neural networks with delays

In this paper, we establish a method to study the mean square exponential stability of the zero solution of impulsive stochastic reaction-diffusion Cohen–Grossberg neural networks with delays. By using the properties of M-cone and inequality technique, we obtain some sufficient conditions ensuring mean square exponential stability of the zero solution of impulsive stochastic reaction-diffusion Cohen–Grossberg neural networks with delays. The sufficient conditions are easily checked in practice by simple algebra methods and have a wider adaptive range. Two examples are also discussed to illustrate the efficiency of the obtained results.     

Construction delay analysis techniques

Construction projects continue to suffer delays. Things go wrong and the project's completion date gets pushed back, with someone to be blamed for it. In practice, attempts are made to identify the causes of delays and schedules are modified to incorporate revised duration and new project time. The analysis itself is usually complex and can be aided by a computerized approach. This paper discusses different delay analysis techniques that are currently used by practitioners in the construction industry. It also discusses a proposed new delay analysis technique called the Isolated Delay Type (IDT). These techniques were tested against a case example and their strengths and weaknesses highlighted. The new technique can be used as a standalone module for delay analysis or could be incorporated within a computer system for construction delay analysis and claims preparation called Computerized Delay Claims Analysis (CDCA) that integrates different software including an expert system and management software such as scheduling and a database or spreadsheet.     

Global exponential stability analysis for cellular neural networks with variable coefficients and delays

Some sufficient conditions for the global exponential stability of cellular neural networks with variable coefficients and time-varying delays are obtained by a method based on a delayed differential inequality. The method, which does not make use of Lyapunov functionals, is simple and effective for the stability analysis of cellular neural networks with variable coefficients and time-varying delays. Some previous results in the literature are shown to be special cases of our results.      

Genetic algorithms for delays evaluation in networked automation systems

In this paper, we present an approach to evaluate end-to-end delays in packets switching networked automation systems. Since Client-Server paradigm is considered for communication between the field devices, the existing methods of network delays evaluation are hardly applicable to assess realistic upper bounds of these delays. In an effort to enhance these delays evaluation, we propose an alternative method. Two algorithms, usually used for optimization problems, exhaustive and genetic algorithms, are then developed to achieve this purpose. While a formal proof about the capacity of the former one to ensure the worst delay overestimation is given, the latter proves to provide faster and more accurate results at the same time. This is shown on a practical case study while comparing the results of the two methods.     

Harmless delays for global exponential stability of Cohen–Grossberg neural networks

In this paper, the Cohen–Grossberg neural networks with time delays are considered without assuming any symmetry of connection matrix and differentiability of the activation functions. By constructing a novel Lyapunov functional, new sufficient criteria for the existence of a unique equilibrium and global exponential stability of the network are derived. These criteria are all independent of the magnitudes of delays, and so the delays under these conditions are harmless. Those results are shown to generalize the previous global exponential stability results derived in the literature.     

Perturbed dynamics of discrete-time switched nonlinear systems with delays and uncertainties

This paper addresses the dynamics of a class of discrete-time switched nonlinear systems with time-varying delays and uncertainties and subject to perturbations. It is assumed that the nominal switched nonlinear system is robustly uniformly exponentially stable. It is revealed that there exists a maximal Lipschitz constant, if perturbation satisfies a Lipschitz condition with any Lipschitz constant less than the maximum, then the perturbed system can preserve the stability property of the nominal system. In situations where the perturbations are known, it is proved that there exists an upper bound of coefficient such that the perturbed system remains exponentially stable provided that the perturbation is scaled by any coefficient bounded by the upper bound. A numerical example is provided to illustrate the proposed theoretical results.     

On computation of stabilizing loop gain and delay ranges for bi-proper delay systems

A graphical method for exactly computing the stabilizing loop gain and delay ranges was proposed [Le BN, Wang Q-G, Lee T-H. Development of D-decomposition method for computing stabilizing gain ranges for general delay systems. J Process Control 2012] for a strictly proper process by determining the boundary functions which may change system׳s stability. A bi-proper process is rare but causes great complications for the method, due to the new phenomena that do not exist for a strictly proper process, such as a non-zero gain at infinity frequency, which may cause infinite intersections of boundary functions within a finite delay range. This paper addresses such a kind of processes and develops a general method that can produce the exact and complete set of the loop gain and delay for closed-loop stabilization, which is hard to find with analytical methods.