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In this article, the robust finite-horizon state estimation problem is investigated for a class of time-varying complex networks (CNs) under the random access protocol (RAP) through available measurements from only a part of network nodes. The underlying CNs are subject to randomly occurring uncertainties, randomly occurring multiple delays, as well as sensor saturations. Several sequences of random variables are employed to characterize the random occurrences of parameter uncertainties and multiple delays. The RAP is adopted to orchestrate the data transmission at each time step based on a Markov chain. The aim of the addressed problem is to design a series of robust state estimators that make use of the available measurements from partial network nodes to estimate the network states, under the RAP and over a finite horizon, such that the estimation error dynamics achieves the prescribed $H_{\infty }$ performance requirement. Sufficient conditions are provided for the existence of such time-varying partial-node-based $H_{\infty }$ state estimators via stochastic analysis and matrix operations. The desired estimators are parameterized by solving certain recursive linear matrix inequalities. The effectiveness of the proposed state estimation algorithm is demonstrated via a simulation example. [ABSTRACT FROM AUTHOR]

In this brief, a new outlier-resistant state estimation (SE) problem is addressed for a class of recurrent neural networks (RNNs) with mixed time-delays. The mixed time delays comprise both discrete and distributed delays that occur frequently in signal transmissions among artificial neurons. Measurement outputs are sometimes subject to abnormal disturbances (resulting probably from sensor aging/outages/faults/failures and unpredictable environmental changes) leading to measurement outliers that would deteriorate the estimation performance if directly taken into the innovation in the estimator design. We propose to use a certain confidence-dependent saturation function to mitigate the side effects from the measurement outliers on the estimation error dynamics (EEDs). Through using a combination of Lyapunov–Krasovskii functional and inequality manipulations, a delay-dependent criterion is established for the existence of the outlier-resistant state estimator ensuring that the corresponding EED achieves the asymptotic stability with a prescribed H∞ performance index. Then, the explicit characterization of the estimator gain is obtained by solving a convex optimization problem. Finally, numerical simulation is carried out to demonstrate the usefulness of the derived theoretical results. [ABSTRACT FROM AUTHOR]

In this article, the recursive filtering problem is investigated for a class of time-varying complex networks with state saturations and random coupling strengths under an event-triggering transmission mechanism. The coupled strengths among nodes are characterized by a set of random variables obeying the uniform distribution. The event-triggering scheme is employed to mitigate the network data transmission burden. The purpose of the problem addressed is to design a recursive filter such that in the presence of the state saturations, event-triggering communication mechanism, and random coupling strengths, certain locally optimized upper bound is guaranteed on the filtering error covariance. By using the stochastic analysis technique, an upper bound on the filtering error covariance is first derived via the solution to a set of matrix difference equations. Next, the obtained upper bound is minimized by properly parameterizing the filter parameters. Subsequently, the boundedness issue of the filtering error covariance is studied. Finally, two numerical simulation examples are provided to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this brief, the partial-neurons-based passivity-guaranteed state estimation (SE) problem is examined for a class of discrete-time artificial neural networks with randomly occurring time delays. The measurement outputs available utilized for the SE are allowed to be available only at a fraction of neurons in the networks. A Bernoulli-distributed random variable is employed to characterize the random nature of the occurrence of time delays. By resorting to the Lyapunov–Krasovskii functional method as well as the stochastic analysis technique, sufficient criteria are provided for the existence of the desired state estimators ensuring the estimation error dynamics to achieve the asymptotic stability in the mean square with a guaranteed passivity performance level. In addition, the parameterization of the estimator gain is acquired by solving a convex optimization problem. Finally, the validity of the obtained theoretical results is illustrated via a numerical simulation example. [ABSTRACT FROM AUTHOR]

Over the past two decades, the synchronization problem for dynamical networks has drawn significant attention due to its clear practical insight in biological systems, social networks, and neuroscience. In the case where a dynamical network cannot achieve the synchronization by itself, the feedback controller should be added to drive the network toward a desired orbit. On the other hand, the time delays may often occur in the nodes or the couplings of a dynamical network, and the existence of time delays may induce some undesirable dynamics or even instability. Moreover, in the course of implementing a feedback controller, the inevitable actuator limitations could downgrade the system performance and, in the worst case, destabilize the closed-loop dynamics. The main purpose of this paper is to consider the synchronization problem for a class of delayed dynamical networks with actuator saturations. Each node of the dynamical network is described by a nonlinear system with a time-varying delay and the intermittent control strategy is proposed. By using a combination of novel sector conditions, piecewise Lyapunov-like functionals and the switched system approach, delay-dependent sufficient conditions are first obtained under which the dynamical network is locally exponentially synchronized. Then, the explicit characterization of the controller gains is established by means of the feasibility of certain matrix inequalities. Furthermore, optimization problems are formulated in order to acquire a larger estimate of the set of initial conditions for the evolution of the error dynamics when designing the intermittent controller. Finally, two examples are given to show the benefits and effectiveness of the developed theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates the variance-constrained $H_{\infty}$ state estimation problem for a class of nonlinear time-varying complex networks with randomly varying topologies, stochastic inner coupling, and measurement quantization. A Kronecker delta function and Markovian jumping parameters are utilized to describe the random changes of network topologies. A Gaussian random variable is introduced to model the stochastic disturbances in the inner coupling of complex networks. As a kind of incomplete measurements, measurement quantization is taken into consideration so as to account for the signal distortion phenomenon in the transmission process. Stochastic nonlinearities with known statistical characteristics are utilized to describe the stochastic evolution of the complex networks. We aim to design a finite-horizon estimator, such that in the simultaneous presence of quantized measurements and stochastic inner coupling, the prescribed variance constraints on the estimation error and the desired $H_{\infty}$ performance requirements are guaranteed over a finite horizon. Sufficient conditions are established by means of a series of recursive linear matrix inequalities, and subsequently, the estimator gain parameters are derived. A simulation example is presented to illustrate the effectiveness and applicability of the proposed estimator design algorithm. [ABSTRACT FROM AUTHOR]