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This paper deals with global synchronization of fractional complex networks with non-delayed and delayed couplings. Applying fractional Razumikhin theorem, a simple quadratic Lyapunov function is constructed and two linear matrix inequality (LMI) criteria on global synchronization are proposed. It is very convenient and efficient to check synchronization of the presented network models by using our proposed method. Finally, numerical simulations are given to show the efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

This paper introduces an effective approach to study the stability of static neural networks with interval time-varying delay using delay partitioning approach and tighter integral inequality lemma. By decomposing the delay interval into multiple equidistant subintervals and multiple nonuniform subintervals, some suitable Lyapunov–Krasovskii functionals are constructed on these intervals. A set of novel sufficient conditions are obtained to guarantee the stability analysis issue for the considered system. These conditions are expressed in the framework of linear matrix inequalities, which heavily depend on the lower and upper bounds of the time-varying delay. It is shown, by comparing with existing approaches, that the delay-partitioning approach can largely reduce the conservatism of the stability results. Finally, three examples are given to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents an autopilot design for a robotic unmanned surface vehicle in the presence of unknown yaw dynamics and measurement noises. A robust adaptive steering law is developed with the aid of a predictor, neural networks, and a modified dynamic surface control technique. Specifically, a predictor together with a low-frequency learning-based neural updating law is developed to identify the unknown yaw dynamics, as well as to reconstruct the states corrupted by measurement noises. Besides, to avoid the noise amplification effect of first-order filter, a linear tracking differentiator is incorporated into the dynamic surface control design approach to produce a noise-tolerant estimate of virtual control derivative. The stability of the closed-loop autopilot system is established by Lyapunov analysis. A salient feature of the developed controller is that it can achieve the desired performance in the presence of model uncertainty and measurement noises, simultaneously. Both simulation and experiment results are given to validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we discuss global asymptotic stability to a BAM neural networks of neutral type with delays. Under the assumptions that the activation functions only satisfy global Lipschitz conditions, a new and complicated LMI condition is established on global asymptotic stability for the above neutral neural networks by means of using Homeomorphism theory, matrix and Lyapunov functional. In our result, the hypotheses for boundedness in and monotonicity in on the activation functions are removed. On the other hand, the LMI condition is also different from those in . Finally, an example is given to show the effectiveness of the theoretical result. [Copyright &y& Elsevier]

Abstract: In this paper, the uniformly boundedness and uniformly ultimately boundedness of high-order neural networks with time-varying delays are studied. Proper Lyapunov functionals and some analytic techniques are employed to derive the sufficient conditions under which the networks proposed are uniformly bounded and uniformly ultimately bounded. The results are shown to improve the results derived in (Arik, 2004). Some examples are given to illustrate the correctness of our results. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the global exponential synchronization of delay-coupled identical genetic oscillator. By constructing appropriate Lyapunov functional and using the linear matrix inequality (LMI) approach, a series of sufficient criteria, which are very easy to verify, are obtained. It is shown that these criteria improve and extend the earlier works. Finally, a population of genetic oscillators based on the Goodwin model is adopted as a numerical example to demonstrate the effectiveness of our theoretical results. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of passivity analysis is investigated for a class of discrete-time stochastic neural networks with time-varying delays. For the neural networks under study, a generalized activation function is considered, where the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By constructing proper Lyapunov–Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique, a delay-dependent passivity condition is derived in terms of linear matrix inequalities (LMIs). Furthermore, when the parameter uncertainties appear in the discrete-time stochastic neural networks with time-varying delays, a delay-dependent robust passivity condition is also presented. An example is given to show the effectiveness of the proposed criterion. [Copyright &y& Elsevier]

Abstract: This paper studies the delay-interval-dependent stability of the equilibrium point of a general class of recurrent neural networks with time-varying delays that may exclude zero. By constructing the appropriate Lyapunov–Krasovskii functional, two sufficient conditions ensuring the global asymptotic stability of the equilibrium point of such networks with interval-time-varying delays are established. The present results, together with two numerical examples, show that the equilibrium points of the considered networks may be globally asymptotically stable in some delay interval(s) even though the equilibrium points of the corresponding delay-free recurrent neural networks are not globally asymptotically stable. [Copyright &y& Elsevier]