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This paper studies the problem how to estimate the reachable set for a class of delayed bidirectional associative memory neural network systems (NNSs), which have Markov switching parameters and unit-energy or unit-peak bounded disturbance inputs. The feature of the Markov jump bidirectional associative memory NNSs shows in the following twofold: the time delay is time varying; the transition rates is time varying. Moreover, the time-varying transition rates is piecewise constant. Using the Lyapunov functional method, delay-partitioning and linear matrix inequalities techniques, the estimate problem of the reachable set depending on time delay is solved. The effectiveness of the given results is illustrated by the proposed numerical examples. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of adaptive neural control for a class of strict-feedback stochastic nonlinear systems with multiple time-varying delays, which is subject to input saturation. Via the backstepping technique and the minimal learning parameters algorithm, the problem is solved. Based on the Razumikhin lemma and neural networks' approximation capability, a new adaptive neural control scheme is developed. The proposed control scheme can ensure that the error variables are semi-globally uniformly ultimately bounded in the sense of four-moment, while all the signals in the closed-loop system are bounded in probability. Two simulation examples are provided to demonstrate the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

In this work, the problem of passivity-based synchronization for coupled chaotic neural networks with randomly occurring time delays is addressed. The topology switching of the networks is subject to the semi-Markov process. Some Bernoulli random variables are used to simulate the randomly occurring phenomena of the aforesaid networks. Furthermore, the main objective is to develop the synchronization criteria such that the considered networks can achieve global stochastic synchronization and conform to the passive performance index. Subsequently, by using some relatively advantageous inequalities and reasonable matrix transformation techniques, some available synchronization criteria are derived. Ultimately, an example is presented to illustrate the applicability and availability of the method proposed. [ABSTRACT FROM AUTHOR]

This work investigates the dissipativity and stability analysis problems for Markov jump generalized neural networks subject to time-varying interval delays. The aim of the present study is to determine whether the new dissipativity and stability criteria with less conservatism could be established for Markov jump generalized delayed neural networks or not. In this connection, a more general dissipative property index inequality is firstly introduced. On basis of fully considering mode-dependent matrices in the Lyapunov–Krasovskii functional, some advantageous negative terms ignored in some existing works are taken into account. By employing some novel integral inequalities and stochastic analysis theory, some available less conservative criteria are developed. Three compared examples are finally shown to explain the reduced conservatism and superiority of the presented criteria for Markov jump generalized delayed neural networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the adaptive finite-time flocking problem for a class of second-order nonlinear multi-agent systems with parameter uncertainties. The states of all agents can achieve finite-time consensus by using the proposed control algorithm under an undirected graph. It is shown that connectivity preservation is guaranteed by choosing an appropriate Lyapunov potential function. A numerical example is presented to illustrate the effectiveness of the proposed consensus protocol. [ABSTRACT FROM AUTHOR]