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The robust filtering problem is tackled for a class of shift-varying two-dimensional systems with uncertain-variance noises under the scheduling of the weighted try-once-discard (WTOD) protocol. The measurements collected from the sensors are transmitted to a remote filter via a shared network. To alleviate the communication burden and obviate the network congestion, the WTOD protocol is adopted to orchestrate the data transmission, where a sensor node is solely permitted to broadcast its information to the remote filter at every transmission step. Moreover, the resilient filter is exploited to regulate the possible gain perturbation. The objective of the addressed problem is to design a robust filter in a recursive structure such that, in the simultaneous presence of the uncertain-variance noises and the WTOD protocol, the minimal upper bounds (UBs) on the filtering error variances (EVs) are developed for the considered system. First, by means of induction and stochastic analysis technique, certain UBs in terms of coupled recursive difference equations are derived for the actual EVs. Then, a proper filter is carefully designed which achieves the minimization of the obtained UBs at each step. Finally, an illustrative example is presented to verify the usefulness of the proposed protocol-based filtering method.

This article tackles the recursive filtering problem for an array of 2-D systems over sensor networks with a given topology. Both the measurement degradations of the network outputs and the stochastic perturbations of network couplings are modeled to reflect engineering practice by introducing some random variables with given statistics. The goal of the addressed problem is to devise the distributed recursive filters capable of cooperatively estimating the true state in order to ensure locally minimal upper bound (UB) on the second-order moment of the filtering error (also viewed as the general error variance). For this purpose, the general error variance regarding the underlying target plant is first provided to facilitate the subsequent filter design, and then a certain UB on the error variance is constructed by exploiting the stochastic analysis and the induction approach. Furthermore, in view of the inherent sparsity of the sensor network, the gain parameters of the desired distributed filters are determined, and the proposed recursive filtering algorithm is shown to be scalable. Finally, an illustrative example is given to demonstrate the validity of the established filtering strategy.

In this paper, a novel communication constraint is introduced for the synchronization problem of coupled agents over finite field networks. Given a network of interconnected agents, the state information of agents may contain multiple levels, therefore multiple communication channels between two connected agents are needed to transmit corresponding level information. Compared with existing results over finite fields, the model considered in this paper is assumed that there are inactive communication channels in all communication channels between two agents, that is, only part of state information can be transmitted. Moreover, due to the disturbance or irregular environmental variation, the switching dynamics of active channels is also considered in the model. The proposed communication constraints bring some difficulties to the analysis of network synchronization since less information transmission between agents. To overcome these difficulties, we introduce a group of channel matrices to describe the activation of communication channels, and a state layered method is utilized to derive the main results. Then, two criteria are obtained to judge the synchronization of agents over finite field networks. Finally, two numerical examples are given to illustrate the effectiveness of our theoretical results.

This paper addresses the dissipativity analysis and controller design for a class of T-S fuzzy positive systems which are described by the Roesser model. First, sufficient conditions obtained by utilizing the minimum dwell time method and co-positive Lyapunov function approach are given to guarantee the considered system is 2-D ( ζ , η )- α -dissipative. Then via the state feedback fuzzy controller and the static output feedback fuzzy controller, criteria are also presented under which the resulting closed-loop systems are positive and 2-D ( ζ , η )- α -dissipative. Moreover, the explicit design schemes for the desired controller parameters are also presented in the form of linear programming. Finally, a simple illustrative example is provided to show the validity of the approach outlined in this article.

In this study, the observer-based output feedback H ∞ control problem is investigated for the two-dimensional (2D) networked control system (NCS) in form of Fornasini–Marchesini second model. The communication from the controller to the actuator is carried out through a shared network medium, where only one signal gets the network access at each transmission instant. To avoid data collisions, the periodic scheduling protocol is employed to assign the network access priority to the actuator in chronological order. To improve the reliability of data transmission, redundant channels are applied to deliver the measurement observed by the sensor to the controller. The aim of the addressed problem is to design an observer-based output feedback controller so that the considered 2D NCS satisfy a predefined H ∞ performance index. Sufficient condition is given to ensure the H ∞ performance of the closed-loop system via the Lyapunov theory and stochastic analysis. Moreover, the observer-based output feedback controller is designed to achieve the desired H ∞ performance index, under which the controller parameters are determined by solving certain optimisation problems. Finally, a numerical example is given to demonstrate the feasibility of the proposed approach.

This study is concerned with the robust H ∞ filtering problem for two-dimensional (2D) systems in Roesser model with randomly occurring non-linearity (RON) and stochastic communication protocol (SCP). The introduced non-linearity is sector-like and governed by a random variable obeying the Bernoulli distribution law. The SCP is depicted by an independent and identically distributed sequence to schedule the signal transmission. The phenomenon of time delay is also taken into consideration. The aim of this study is to design a filter such that the augmented filtering error system satisfies a predefined H ∞ performance index. Sufficient condition is first given to ensure the robust stability of the augmented filtering error system via Lyapunov theory and stochastic analysis. Then, the result is extended to guarantee the H ∞ performance of the augmented system under SCP, where sufficient criterion related to the transmission probability is given in terms of matrix inequality. Moreover, the filter gains are explicitly designed which enable the system to achieve the H ∞ filtering performance. Finally, a numerical example is given to show the effectiveness of the proposed design method.

In this paper, the H∞ control problem is investigated for a time-delayed two-dimensional (2-D) network under the random access protocol (RAP). The control input is transmitted to the actuator via a network with communication constraint, where only one signal obtains the network access at each transmission instant, and the RAP is adopted here to schedule the data transmission which is depicted by an independent and identically distributed sequence. The randomly occurring uncertainty, nonlinearity and unknown time delays are all introduced in the underlying 2-D system. The aim of the addressed problem is to design an observer-based controller so that the considered 2-D system has a predefined H∞ performance index. Sufficient condition is given to ensure the H∞ performance of the closed-loop system via the Lyapunov theory and stochastic analysis. Moreover, the observer-based controller is dexterously designed to ensure the achievement of the H∞ performance, under which the controller gains are designed by solving certain matrix inequalities. Finally, a numerical example is given to demonstrate effectiveness of the proposed approach.

The robust dissipativity problem is analysed in this article for the Markovian switching complex-valued neural networks perturbed by stochastic noises, where the transition rates of the Markovian s...

In the past few decades, both theoretical developments and practical applications of the two-dimensional (2-D) systems have received considerable research interest due to their clear engineering insights in many industrial branches. In order to estimate the actual system states from available and possibly noisy measurements, various filtering strategies have been proposed in the existing literature which include, but are not limited to, Kalman filtering, variance-constrained filtering, H∞ filtering, l2-l∞ filtering, l1 filtering, dissipative filtering, and protocol-based filtering approaches. In particular, the filtering issues of 2-D systems subjected to disturbances/noises from different sources have drawn much research attention. The intent of this survey is to not only provide a comprehensive review of the latest results but also bestow some in-depth insights on the future research with respect to the filter design problems for 2-D systems under various performance requirements. The fundamentals of the 2-D systems are first presented and the corresponding filtering techniques are introduced from the aspects of engineering insights and physical interpretations. Then, several well-known 2-D systems are recalled and discussed, and some other 2-D systems with more complicated dynamics are surveyed. Subsequently, the recent advances in designing appropriate 2-D filtering algorithms under specific filtering performance indices are reviewed. Finally, this paper is concluded with some possible future research topics outlined on the 2-D filtering schemes.

This paper is concerned with the local synchronization problem for the interconnected Boolean networks (BNs) without and with stochastic disturbances. For the case without stochastic disturbances, first, the limit set and the transient period of the interconnected BNs are discussed by resorting to the properties of the reachable set for the global initial states set. Second, in terms of logical submatrices of a certain Boolean vector, a compact algebraic expression is presented for the limit set of the given initial states set. Based on it, several necessary and sufficient conditions are derived assuring the local synchronization of the interconnected BNs. Subsequently, an efficient algorithm is developed to calculate the largest domain of attraction. As for the interconnected BNs with stochastic disturbances, first, mutually independent two-valued random logical variables are introduced to describe the stochastic disturbances. Then, the corresponding local synchronization criteria are also established, and the algorithm to calculate the largest domain of attraction is designed. Finally, numerical examples are employed to illustrate the effectiveness of the obtained results/ algorithms.

This paper is concerned with the robust finite-horizon filter design problem for a class of two-dimensional (2-D) time-varying systems with norm-bounded parameter uncertainties and incomplete measurements. The incomplete measurements cover randomly occurring sensor delays and missing measurements that are presented in a unified form by resorting to a stochastic Kronecker delta function. The occurrences of the sensor delays and missing measurements are governed by stochastic variables with known probability distributions. The main aim of the addressed problem is to design a recursive filter with appropriate gain parameters that ensure the local minimum of certain upper bound on the estimation error variance at each time instant. With the aid of the inductive approach and the 2-D Riccati-like difference equations, one of the first few attempts is made to tackle the robust filter design problem for 2-D uncertain systems with random sensor delays over a finite horizon. Sufficient conditions are provided for the existence of an upper bound on the estimation error variance, an algorithm is then developed to derive such an upper bound, and finally the desired filter is designed to minimize the obtained upper bound. The filter design procedure is of a recursive form that facilitates the online calculation. A numerical simulation is carried out to show the effectiveness of the developed filtering scheme.

In this paper, the non-fragile dissipative filtering problem is investigated for the two-dimensional (2-D) systems subjected to randomly occurring uncertainties and redundant channel protocol. For the redundant channel transmission, if a signal fails to be transmitted through a certain channel, the next channel is immediately activated to transmit the signal once again. The norm-bounded uncertainties are introduced in the considered system, which are governed by two stochastic variables obeying the Bernoulli distribution law. The aim of this paper is to design a dissipative filter in a non-fragile manner such that the augmented filtering error system is not only asymptotically stable in the mean-square sense but also satisfies a strict 2-D ( Q , S , R ) − α -dissipativity performance index. Based on the Lyapunov theory and stochastic analysis, sufficient conditions are first given to guarantee the asymptotic stability of the 2-D system in the mean-square sense. Then, the non-fragile filter is designed to ensure that the 2-D system satisfies the strict 2-D ( Q , S , R ) − α -dissipativity performance index, under which the expressions of the non-fragile filter gains are given by solving certain matrix inequalities. Finally, a simulation example is provided to show the effectiveness of the proposed dissipative filtering method.

Sensor resolution, which is one of the most important parameters/specifications for almost all kinds of sensors, plays an important role in any signal processing problems. This paper deals with the distributed filtering problem for a class of discrete time-varying stochastic systems subject to non-logarithmic sensor resolution and stochastic nonlinearities. The soft measurement technique is exploited in the filter design to overcome the difficulties resulting from the sensor-resolution-induced (SRI) uncertainty. The aim of the presented filtering problem is to construct the distributed filter over a sensor network such that, in the presence of SRI uncertainty and stochastic nonlinearity, an upper bound on the filtering error covariance is guaranteed and subsequently minimized by appropriately designing the filer parameters at each time instant. Moreover, a matrix simplification method is utilized to tackle the difficulties stemming from the sparsity of sensor networks. Finally, a numerical example is employed to illustrate the effectiveness of the proposed filtering scheme.

This article is concerned with the local stabilization of memristive neural networks subject to actuator saturation via aperiodic sampled-data control. A dynamic partitioning point is elegantly introduced, which is placed between the latest sampling instant and the present time to utilize more information of the inner sampling. To analyze the stability of the closed-loop system, a time-dependent two-side looped functional, which fully utilizes the state information on the entire sampling interval as well as at the dynamic partitioning point, is constructed. It relaxes the positive definiteness of traditional Lyapunov functional inside the sampling interval and therefore, provides the possibility to derive less conservative stability results. Besides, an auxiliary system is established to describe the dynamics at the partitioning point. On the basis of the constructed looped functional, the discrete-time Lyapunov theorem, and some estimation approaches, a linear matrix inequalities-based stability criterion is developed, and then, the sampled-data saturated controller is designed to ensure the local asymptotic stability of the closed-loop system. Thereafter, two optimization problems are developed to seek the desired feedback gain and to expand the estimation of the region of attraction or to enlarge the upper bound of the sampling interval. Eventually, a numerical example is given to demonstrate the effectiveness and the superiority of the proposed results.

This paper investigates the model evaluation problem for the stochastic Boolean control networks (SBCNs). First, an algebraic expression of the SBCN is obtained based on the semi-tensor product method, and a straightforward approach is then proposed to compute the probability that the given observed output sequence is produced by the considered model. Second, two recursive algorithms, namely the forward and the backward algorithms, are designed for model evaluation by resorting to the forward-backward technique. In addition, scaling factors are introduced to deal with the numerical issues arising in the implementation of the developed algorithms. Finally, to illustrate the applicability and effectiveness of the proposed algorithms, a Boolean model of the lac operon is employed as an example for numerical simulation.

This paper addresses the recursive minimum-variance filtering problem for a class of two-dimensional shift-varying systems with stochastic nonlinearity and degraded measurements. The stochastic nonlinearity is governed by its statistical characteristics and the degraded measurements reflect the signal degradation obeying certain prescribed probabilistic distributions. The main objective of this paper is to construct a two-step recursive filter that achieves the minimum error variance of the state estimation at each step. Utilizing an inductive approach, unbiasedness of the proposed filter is first ensured and the parameters of the filter are then designed by resorting to the completing squares method. Subsequently, the filtering performances including the boundedness and the monotonicity are investigated with respect to the measurement degradations through mathematically rigorous analysis. Moreover, a computational algorithm is presented to facilitate the online implementation of the designed filter. Finally, numerical simulation illustrates the effectiveness and applicability of the proposed filtering scheme in the state estimation problem for monitoring a long transmission line in circuit systems.

In this study, the consensus problem is investigated for the multi-agent systems with antagonistic interactions and communication delay under the event-triggered control protocol. For the directed signed networks, an event-based interaction protocol is first proposed. Then it is shown that with the proposed protocol, bipartite consensus can be realised for the structurally balanced signed networks with distributed communication delay. As for the structurally unbalanced signed networks with communication delay, it is proved that consensus can also be reached under the proposed event-triggered scheme and the final consensus value is zero. It should be emphasised that the proposed event-triggered controllers depend only on the local information. Furthermore, Zero phenomenon can be excluded, and the positive lower bound of the inter-event times is simultaneously provided. Finally, the effectiveness of proposed event-triggered schemes is demonstrated by one numerical example.

This paper investigates the state estimation problem for a class of 2-D time-varying systems with error variance constraints, where the implemented estimator gain is subject to stochastic perturbations. Redundant channels are utilized as a protocol to strengthen the transmission reliability and the channels' packet dropout rates are described by mutually uncorrelated Bernoulli distributions. The objective of the addressed problem is to design a resilient estimator such that an upper bound on the estimation error variance is first guaranteed and then minimized at each time step, where the considered gain perturbations are characterized by their statistical properties. By employing the induction method and the variance-constrained approach, an upper bound on the estimation error variance is first constructed by means of the solutions to two Riccati-like difference equations and, subsequently, a locally minimal upper bound is achieved by appropriately designing the gain parameter. Then, an effective algorithm is proposed for designing the desired estimator, which is in a recursive form suitable for online applications. Finally, a numerical simulation is provided to demonstrate the usefulness of the proposed estimation scheme.

In this paper, the robust H∞ state estimator is designed for the two-dimensional (2-D) systems in Fornasini-Machesini second model under logarithmic quantization and Round-Robin protocol. The norm-bounded uncertainties are also involved in the considered system, which are governed by two stochastic variables satisfying the Bernoulli distribution. As a kind of static scheduling, the Round-Robin protocol assigns the communication access rights to the sensors in the chronological order. The aim of this paper is to design a state estimator so that the addressed estimation error system satisfies a predefined H∞ performance index. Sufficient conditions are given to ensure the 2-D augmented error system to be robustly stable via intensive stochastic analysis, then the results are further extended to achieve the robust H∞ stability for the augmented error system. Explicit expressions of the estimator gains are also given by solving certain matrix inequalities. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed estimation scheme.

This paper addresses the finite-time asymmetric bipartite consensus problem for multi-agent systems (MAS) associated with signed graphs. Different from bipartite consensus in the previous literatures, asymmetric bipartite consensus means that the states of all agents will converge to two values with different signs and modulus. Two distinct nonlinear consensus control protocols are constructed for the considered system to achieve the finite-time asymmetric bipartite consensus and the fixed-time asymmetric bipartite consensus, respectively. Under the first proposed protocol, it is shown that within a finite time, the considered system can realize asymmetric bipartite consensus. To strengthen the obtained result, the second protocol is proposed, which guarantees that all agents could achieve the asymmetric bipartite consensus in fixed time, that is, the finite-time consensus can be reached before a settling time which is irrelevant with the initial states of the agents. Finally, numerical simulations are given to verify effectiveness of the proposed consensus control protocols.

Security control aims to guarantee the consensus of multiagent systems (MASs) in the presence of the denial-to-service attacker. Most of the existing distributed controllers are invalid during the attack interval due to the paralyzed communication channels. In order to overcome this difficulty, a novel hybrid distributed control protocol is designed. Here, the controller uses the latest information saved in the buffers in the presence of malicious attacks, which will further enhance the security of MASs. Some sufficient conditions on the coupled strength and attack parameters are derived to achieve the leader-following consensus of MASs. Furthermore, we estimate the upper bounds of denial-of-service (DoS) frequency and DoS duration which the MASs can tolerate before losing consensus. Notice that we also reduce the computational complexity via the property of the Kronecker product. Besides, an observer-based model is proposed and the corresponding consensus criterion is established to reduce the effects of attackers on the controller. Finally, the efficiency of our theoretical results is illustrated by a numerical example.

In this article, a general theoretical framework is developed for the state estimation problem of stochastic time-varying Boolean networks (STVBNs). The STVBN consists of a system model describing the evolution of the Boolean states and a model relating the noisy measurements to the Boolean states. Both the process noise and the measurement noise are characterized by sequences of mutually independent Bernoulli distributed stochastic variables taking values of 1 or 0, which imply that the state/measurement variables may be flipped with certain probabilities. First, an algebraic representation of the STVBNs is derived based on the semitensor product. Then, based on Bayes’ theorem, a recursive matrix-based algorithm is obtained to calculate the one-step prediction and estimation of the forward–backward state probability distribution vectors. Owing to the Boolean nature of the state variables, the Boolean Bayesian filter is designed that can be utilized to provide the minimum MSE state estimate for the STVBNs. The fixed-interval smoothing filter is also obtained by resorting to the forward–backward technique. Finally, a simulation experiment is carried out for the context estimation problem of the $p53$ - $MDM2$ negative-feedback gene regulatory network.

In this paper, the robust H∞ state estimation problem is investigated for a class of discrete-time delayed bidirectional associative memory neural networks with randomly occurring uncertainties (ROUs) and sensor saturations. In order to more clearly reflect the reality in a networked environment, Bernoulli distributed white sequences with known probabilities are introduced to characterize the network-induced phenomena of the ROUs and the randomly occurring sensor saturations. The purpose of the addressed problem is to design a robust H∞ state estimator such that the dynamics of the estimation error is asymptotically stable in the mean square. By using matrix computation and stochastic analysis approaches, a sufficient condition is established to ensure the desired H∞ performance requirements, and the explicit form of the estimator gains can be obtained by solving some matrix inequalities. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed state estimation approach.

This study deals with the bipartite consensus problem for multi-agent systems associated with signed digraphs. For structurally balanced signed digraphs, by constructing a new class of general Laplacian matrices, it is found that all agents will converge to two values with different modulus if the signed digraph is strongly connected. Interestingly, these two values completely depend on the left eigenvector of the general Laplacian matrix corresponding to the zero eigenvalue and the initial states of all agents. Furthermore, it is shown that all agents can reach interval asymmetric bipartite consensus if the associated signed digraph contains a spanning tree. In particular, some useful results are also presented for specific signed digraphs with spanning trees. Finally, two numerical examples are provided to demonstrate the effectiveness of the main results.

In this paper, controller synthesis problem is investigated for a class of Fornasini–Marchesini second type switched systems with time-varying delays under the Takagi–Sugeno fuzzy rules. Both the cases of state feedback controller and static output feedback controller are designed. By utilizing the common Lyapunov function approach and the multiple Lyapunov functions method, sufficient criteria are derived in the form of linear matrix inequalities to assure the resulting closed-loop 2-D systems to be positive and asymptotically stable, which can be easily computed using the existing numerical algorithms. Explicit design schemes for the state feedback/static output feedback controllers gain matrices are also presented. Finally, advantage and effectiveness of the proposed criteria are shown by a numerical example.

This paper is concerned with the robust H∞ state estimation problem for a class of uncertain genetic regulatory networks (GRNs) with random delays and external disturbances by using sample-data method. An important feature of this paper is that the time-varying delays are assumed to be random and their probability distributions are known a priori. By substituting the continuous measurements, the sampled measurements are used to estimate the concentrations of mRNAs and proteins. On the basis of the extended Wirtinger inequality, a discontinuous Lyapunov functional is introduced. Then, some sufficient conditions are derived in terms of a set of linear matrix inequalities (LMIs), which ensure that the error system is globally asymptotically stable in the meansquare sense and satisfies H∞ performance. Further, the explicit expression of the required estimator gain matrices is proposed. Finally, a numerical example is used to illustrate the effectiveness and feasibility of the obtained estimation method.

This paper investigates the synchronization problem for the realization-dependent probabilistic Boolean networks (PBNs) coupled unidirectionally in the drive-response configuration. The realization of the response PBN is assumed to be uniquely determined by the realization signal generated by the drive PBN at each discrete time instant. First, the drive-response PBNs are expressed in their algebraic forms based on the semitensor product method, and then, a necessary and sufficient condition is presented for the synchronization of the PBNs. Second, by resorting to a newly defined matrix operator, the reachable set from any initial state is expressed by a column vector. Consequently, an easily computable algebraic criterion is derived assuring the synchronization of the drive-response PBNs. Finally, three illustrative examples are employed to demonstrate the applicability and usefulness of the developed theoretical results.

The resilient filtering problem is considered for a class of time-varying networks with stochastic coupling strengths. An event-triggered strategy is adopted to save the network resources by schedu...

This paper is concerned with the dynamic output-feedback controller design for the positive Roesser type nonlinear system, which is intrinsically characterized by the switched mechanism and subtly decomposed into the linear form under the Takagi–Sugeno fuzzy rules. Firstly, based on the co-positive Lyapunov function and the average dwell time method, sufficient conditions are presented, under which the resulting closed-loop system is exponentially stable and has l 1 -gain bound γ . Then, explicit expressions are also given to derive the expected controller gain matrices with desired l 1 -gain bound. Finally, effectiveness of the proposed results is illustrated by two numerical examples.

Accurate information about the train position and velocity is critically important for Communication-based Train Control (CBTC) systems. However, it is practically difficult to obtain the precise information of such information due mainly to the “inaccurate measurements” induced by the measurement noises and the “unreliable communication” caused by the wireless train-ground communication. In this paper, a recursive filtering algorithm is proposed to generate the estimates of the train position and velocity for CBTC systems subject to the measurement noise and packet dropouts. Firstly, the dynamics of a train is modeled based on the Newton’s motion equation. Then, a Bernoulli distributed sequence is introduced to describe the packet dropout phenomenon of the wireless communication. The purpose of the problem addressed is to design a recursive filter such that there exists an upper bound for the filtering error covariance. Subsequently, such an upper bound is minimized by properly designing the filter parameter recursively. The desired filter parameter is obtained by solving two Riccati-like difference equations that are of a recursive form suitable for online applications. Finally, an illustrative example is given to show the effective of the proposed filter design scheme.

This paper is concerned with the finite-time stability and the finite-time boundedness issues on the estimation problem for a class of continuous-time uncertain recurrent neural networks with Markovian jumping parameters. The uncertain parameters are described by the linear fractional uncertainties and the jumping parameters obey the homogeneous Markov process with possibly deficient probability transition matrix. A full-order state estimator is constructed to estimate the neuron state, in presence of the uncertain and jumping parameters, such that the resulting error dynamics of the state estimation is (i) finite-time stable in the disturbance-free case; and (ii) finite-time bounded in case of exogenous disturbances on the measurements. By employing the Lyapunov stability theory and stochastic analysis techniques, sufficient conditions are established that ensure the existence of the desired finite-time state estimator, and then the explicit expression of such state estimators is characterized in terms of the feasibility to a convex optimization problem that can be easily solved by using the semi-definite programme method. Validity and effectiveness of the developed design method are demonstrated by a numerical example.

This paper considers the global exponential synchronization of drive-response memristive neural networks (MNNs) with heterogeneous time-varying delays. Because the parameters of MNNs are state-dependent, the MNNs may exhibit unexpected parameter mismatch when different initial conditions are chosen. Therefore, traditional robust control scheme cannot guarantee the synchronization of MNNs. Under the framework of Filippov solution, the drive and response MNNs are first transformed into systems with interval parameters. Then suitable controllers are designed to overcome the problem of mismatched parameters and synchronize the coupled MNNs. Based on some novel Lyapunov functionals and interval matrix inequalities, several sufficient conditions are derived to guarantee the exponential synchronization. Moreover, adaptive control is also investigated for the exponential synchronization. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.

Gold immunochromatographic strip (GICS) assay provides a quick, convenient, single-copy and on-site approach to determine the presence or absence of the target analyte when applied to an extensive variety of point-of-care tests. It is always desirable to quantitatively detect the concentration of trace substance in the specimen so as to uncover more useful information compared with the traditional qualitative (or semi-quantitative) strip assay. For this purpose, this paper is concerned with the GICS image denoising and deblurring problems caused by the complicated environment of the intestine/intrinsic restrictions of the strip characteristics and the equipment in terms of image acquisition and transmission. The gradient projection approach is used, together with the total variation minimization approach, to denoise and deblur the GICS images. Experimental results and quantitative evaluation are presented, by means of the peak signal-to-noise ratio, to demonstrate the performance of the combined algorithm. The experimental results show that the gradient projection method provides robust performance for denoising and deblurring the GICS images, and therefore serves as an effective image processing methodology capable of providing more accurate information for the interpretation of the GICS images.

In this paper, a hybrid learning approach, which combines the extreme learning machine (ELM) with a new switching delayed PSO (SDPSO) algorithm, is proposed for the problem of the short-term load forecasting (STLF). In particular, the input weights and biases of ELM are optimized by a new developed SDPSO algorithm, where the delayed information of locally best particle and globally best particle are exploited to update the velocity of particle. By testing the proposed SDPSO-ELM in a comprehensive manner on a tanh function, this approach obtain better generalization performance and can also avoid adding unnecessary hidden nodes and overtraining problems. Moreover, it has shown outstanding performance than other state-of-the-art ELMs. Finally, the proposed SDPSO-ELM algorithm is successfully applied to the STLF of power system. Experiment results demonstrate that the proposed learning algorithm can get better forecasting results in comparison with the radial basis function neural network (RBFNN) algorithm.

In this paper, a delayed predator-prey diffusion system with Neumann boundary condition is addressed. By complex computation and rigorous analysis, it is obtained that the unique positive equilibrium of the system under consideration is a Bogdanov-Takens (B-T) singularity under certain conditions, whereas this is not true for the corresponding system without diffusion. By using the normal form theory and the center manifold reduction theorem for partial functional differential equations (PFDEs), the normal form of the B-T singularity is provided. Finally, numerical simulations are carried out to illustrate the theoretical results presented.

This paper investigates the complete synchronization problem for the drive-response switched Boolean networks (SBNs) under arbitrary switching signals, where the switching signals of the response SBN follow those generated by the drive SBN at each time instant. First, the definition of complete synchronization is introduced for the drive-response SBNs under arbitrary switching signals. Second, the concept of switching reachable set starting from a given initial state set is put forward. Based on it, a necessary and sufficient condition is derived for the complete synchronization of the drive-response SBNs. Last, we give a simple algebraic expression for the switching reachable set in a given number of time steps, and two computable algebraic criteria are obtained for the complete synchronization of the SBNs. A biological example is given to demonstrate the effectiveness of the obtained main results.

This study investigates the mean square exponential (MSE) synchronisation problem for the multi-agent systems with multiplicative noises, where the communication topologies among the agents are directed and time varying. The phenomenon of randomly varying coupling delay is considered and described by a Bernoulli distributed white sequence with known probability, thereby better reflecting the practical system dynamics. Network topologies proposed in this study are directed and time varying which intrinsically characterise the random communications between agents. By exploiting the graph theory and the coordinate transformation method, the MSE synchronisation problem for the stochastic multi-agent network is converted into a robust MSE stability problem for the transformed system. Then by constructing a Lyapunov functional and utilising the properties of Kronecker product, both delay-independent and delay-dependent sufficient conditions are derived guaranteeing the transform system to be robustly stable in the mean square. The criteria established are in the form of matrix inequalities which can be solved and checked easily. Finally, simulation examples are provided to illustrate the validity for the obtained results.

This paper addresses the recursive filtering problem for shift-varying linear repetitive processes (LRPs) with limited network resources. To reduce the resource occupancy, a novel event-triggered strategy is proposed where the concern is to broadcast those necessary measurements to update the innovation information only when certain events appear. The primary goal of this paper is to design a recursive filter rendering that, under the event-triggered communication mechanism, an upper bound (UB) on the filtering error variance is ensured and then optimized by properly determining the filter gains. As a distinct kind of 2-D systems, the LRPs are cast into a general Fornasini–Marchesini model by using the lifting technique. A new definition of the triggering-shift sequence is introduced and an event-triggered rule is then constructed for the transformed system. With the aid of mathematical induction, the filtering error variance is guaranteed to have a UB which is subsequently optimized with appropriate filter parameters via solving two series of Riccati-like difference equations. Theoretical analysis further reveals the monotonicity of the filtering performance with regard to the event-triggering threshold. Finally, an illustrative simulation is given to show the feasibility of the designed filtering scheme.

This paper is concerned with the recursive filtering problem for a class of nonlinear 2-D time-varying systems with degraded measurements over a finite horizon. The phenomenon of measurement degradation occurs in a random way depicted by stochastic variables satisfying certain probabilities distributions. The nonlinearities under consideration are dealt with through the Taylor expansion, where the high-order terms of the linearization errors are characterized by norm-bounded parameter uncertainties. The objective of the addressed problem is to design a filter which guarantees an upper bound of the estimation error variance and subsequently minimizes such a bound with the desired gain parameters. By means of mathematical induction, an upper bound is first derived for the estimation error variance by constructing two sets of Riccati-like difference equations, and then the obtained bound is minimized by properly selecting the filter parameter at each time step. Both the minimal upper bound and the desired filter parameter are suitable for recursive online computation. Furthermore, the effect of the stochastic measurement degradation on the filtering performance is discussed. Finally, a simulation example is presented to demonstrate the effectiveness of the designed filter.

The mean square exponential (MSE) synchronization problem is investigated in this paper for complex networks with simultaneous presence of uncertain couplings and incomplete information, which comprise both the randomly occurring delay and the randomly occurring non-linearities. The network considered is uncertain with time-varying stochastic couplings. The randomly occurring delay and non-linearities are modelled by two Bernoulli-distributed white sequences with known probabilities to better describe realistic complex networks. By utilizing the coordinate transformation, the addressed complex network can be exponentially synchronized in the mean square if the MSE stability of a transformed subsystem can be assured. The stability problem is studied firstly for the transformed subsystem based on the Lyapunov functional method. Then, an easy-to-verify sufficient criterion is established by further decomposing the transformed system, which embodies the joint impacts of the single-node dynamics, the network t...

In this paper, we consider the ${H_{\infty }}$ control problem for a class of 2-D Takagi–Sugeno fuzzy described by the second Fornasini–Machesini local state-space model with time-delays and missing measurements. The state delays are allowed to be time-varying within a known interval. The measurement output is subject to randomly intermittent packet dropouts governed by a random sequence satisfying the Bernoulli distribution. The purpose of the addressed problem is to design an output-feedback controller such that the closed-loop system is globally asymptotically stable in the mean square and the prescribed ${H_\infty }$ performance index is satisfied. By employing a combination of the intensive stochastic analysis and the free weighting matrix method, several delay-range-dependent sufficient conditions are presented that guarantee the existence of the desired controllers for all possible time-delays and missing measurements. The explicit expressions of such controllers are derived by means of the solution to a class of convex optimization problems that can be solved via standard software packages. Finally, a numerical simulation example is given to demonstrate the applicability of the proposed control scheme.