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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.

Regularization parameter and kernel parameters play a crucial role on the accuracy and rapidity of the least squares support vector machine (LS-SVM) algorithm. Existing methods of tuning the parameters for LS-SVM have some disadvantages, such as difficulty of getting global optimum and poor convergent speed. In this work, we develop the parameter design of LS-SVM using quantum particle swarm optimization (QPSO) and apply the designed LS-SVM into node localization. According to the initial values, the optimal parameter values are obtained using the leave-10-out cross validation method. Simulation results show that the proposed method is compared to the coupling simulated annealing (CSA) LS-SVM and the improved particle swarm optimization (IPSO) LS-SVM.

In this paper, the problem of exponential synchronization for a class of chaotic neural networks which covers the Hopfield neural networks and cellular neural networks with reaction-diffusion terms and time-varying delays is investigated. A feedback control gain matrix is derived to achieve the state synchronization of two identical neural networks with reaction-diffusion terms, and the synchronization condition can be verified if a certain Hamiltonian matrix with no eigenvalue on the imaginary axis.

This paper addresses stability properties of linear switched positive systems composed of continuous-time subsystems and discrete-time subsystems. Based on the common linear copositive Lyapunov functions, stability of the positive systems is discussed under arbitrary switching. Moreover, a sufficient condition on the minimum dwell time that guarantees the stability of linear switched positive systems. The dwell time analysis interprets the stability of linear switched positive systems through the distance between the eigenvector sets. Thus, an explicit relation in view of stability is obtained between the family of the involved subsystems and the set of admissible switching signals.

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.

In this paper, the global robust dissipativity of integro-differential systems modeling 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 the global robust dissipativity. The results are shown to improve the previous global dissipativity results derived in the literature. Some examples are given to illustrate the correctness of our results.

The passivity conditions for stochastic neural networks with time-varying delays and random abrupt changes are considered in this paper. Sufficient conditions on passivity of stochastic neural networks with time-varying delays and random abrupt changes are developed in the linear matrix inequality (LMI) setting. The results obtained in this paper improve and extend some of the previous results.

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.

The global robust asymptotic stability of bi-directional associative memory (BAM) neural networks with constant or time-varying delays is studied. An approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problem. Some a criteria for the global robust asymptotic stability, which gives information on the delay-dependent property, are derived. Some illustrative examples are given to demonstrate the effectiveness of the obtained results.

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.

The global asymptotic stability of delay bi-directional associative memory neural networks with impulses are studied by constructing suitable Lyapunov functional. Sufficient conditions, which are independent to the delayed quantity, are obtained for the global asymptotic stability of the neural networks. Some illustrative examples are given to demonstrate the effectiveness of the obtained results.

The global asymptotic stability of bi-directional associative memory neural networks with distributed delays and reaction–diffusion terms are studied by using the analysis technique and Lyapunov functional. A sufficient condition is proposed. Two numerical examples are given to show the correctness of our analysis.

By constructing suitable Lyapunov functional and using matrix theory, the global asymptotic stability of delay bi-directional associative memory neural networks with impulses is studied. This paper gives a sufficient condition which is independent with the delayed quantity for the global asymptotic stability of these networks. An illustrative example is given to demonstrate the effectiveness of the obtained results.

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.