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In this study, the iterative learning control (ILC) method is considered for tracking control of a class of distributed parameter systems (DPSs) based on sensor-actuator networks (SANs) with the unknown exogenous input and the measurement noise, which are described by a semi-linear parabolic partial differential equation. The D-type ILC algorithm is presented to control DPSs with non-collocated SANs. When the unknown exogenous input and the measurement noise are bounded, the upper bounds of output errors are obtained via the Bellman-Gronwall lemma and semi-group theory, respectively. The authors prove that the output errors converge to zero in the absence of the unknown exogenous input and the measurement noise. Two examples are given to show the effectiveness of the proposed D-type ILC scheme.

In this paper, the exponential stability of Cohen-Grossberg neural networks with distributed delays is investigated. Using the M-matrix property, the necessary and sufficient condition for the existence, uniqueness and exponential stability of the equilibrium point of the neural networks is obtained. We avoid the difficulty of proving the existence of a positive invariant set as in some previous works.

Some sufficient conditions for the ultimate boundedness and global exponential stability of a class of nonautonomous reaction–diffusion cellular neural networks (RCNNs) with distributed delays are obtained by means of the Lyapunov functional method. Without assuming that the activation functions fijl(⋅) are bounded, the results extend and improve the earlier publications.

This paper deals with the problem of delay-dependent robust stability for a class of switched Hopfield neural networks with time-varying structured uncertainties and time-varying delay. Some Lyapunov-Krasovskii functionals are constructed and the linear matrix inequality (LMI) approach and free weighting matrix method are employed to devise some delay-dependent stability criteria which guarantee the existence, uniqueness and global exponential stability of the equilibrium point for all admissible parametric uncertainties. By using Leibniz-Newton formula, free weighting matrices are employed to express this relationship, which implies that the new criteria are less conservative than existing ones. Some examples suggest that the proposed criteria are effective and are an improvement over previous ones.

This paper discusses a generalized model of high-order Hopfield-type neural networks with time-varying delays. Some novel global stability criteria of the system is derived by using Lyapunov method, linear matrix inequality (LMI) and analytic technique. The LMI-based criteria obtained here are computationally more flexible and more generic than many other existing criteria. A numerical example is given to illustrate our result.

This letter considers the problem of asymptotic stability of Cohen-Grossberg neural networks with time-varying delays. The stability condition is given in terms of a linear matrix inequality (LMI). Comparison between our results and previous results admits that our results establish a new set of stability criteria for delayed Cohen-Grossberg neural networks.

In this paper, the problem of stochastic stability for a class of time-delay Hopfield neural networks with Markovian jump parameters is investigated. The jumping parameters are modeled as a continuous-time, discrete-state Markov process. Without assuming the boundedness, monotonicity and differentiability of the activation functions, some results for delay-dependent stochastic stability criteria for the Markovian jumping Hopfield neural networks (MJDHNNs) with time-delay are developed. We establish that the sufficient conditions can be essentially solved in terms of linear matrix inequalities.

In this paper, the problem of asymptotic synchronization for a class of neural networks with reaction-diffusion terms and time-varying delays is investigated. Using the drive-response concept, a control law is derived to achieve the state synchronization of two identical neural networks with reaction-diffusion terms. Moreover, we derive a sufficient asymptotic synchronization condition for the neural networks with reaction-diffusion terms if reaction-diffusion terms satisfy a weaker condition. The synchronization condition is easy to verify and relies on the connection matrix in the driven networks and the suitable designed controller gain matrix in the response networks.