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X-ray diffraction technology (XRD) is one of the common means of mineral separation, and peak identification and comparison is a key step. The symmetric zero-area conversion method (SZAC) performs well in peak searching of spectral lines, but its wide application is limited by problems such as inaccurate identification results in specific scenes and dependence of parameter selection on spectral data. In this article, an improved symmetric zero-area conversion method (ISZAC) is proposed by combining the ideas of data fusion and passive anti-counterfeiting. The comparison method and flat peak screening were introduced, and the function group was designed to convert the spectral data after background deduction and intensity screening. The results were fused to obtain the peak address determination. Simulation and experimental results show that the improved method has higher accuracy and applicability. In addition, the method is easy to expand, and the conversion function group can be adjusted according to the spectral data characteristics to improve the recognition efficiency.

The main purpose of this article is to investigate the consensus of linear multiagent networks with time-varying characteristics under sampled-data communications, where the time-varying characteristics include both time-varying topologies and the node's linear time-varying dynamics. By using the decoupling method, we prove that the sampled-data consensus problem of multiagent networks is equal to the stability problem of sampled-data systems. Then, the globally asymptotical consensus is investigated for multiagent networks with time-varying characteristics by virtue of the Lyapunov function method. It should be noted that when the Lyapunov function method is utilized to investigate the stability problem of control systems, it is always assumed that the derivative of the constructed Lyapunov function is not more than zero. This assumption is removed here and as a replacement, the average value of the derivative of the Lyapunov function in a period to be negative is needed.

In this paper, the stochastic quaternion-valued neural networks model with variable coefficients and neutral delays is considered, and the mean-square stability criterion is provided via the method of mathematical analysis. In deriving stability criterion, the considered stochastic quaternion-valued neural networks model is implemented as an entirety form without separating the model into two complex-valued or four real-valued models. And the obtained result is valid for stochastic real-valued and complex-valued neural networks. A numerical simulation example is given to show the effectiveness of the obtained result.

In this paper, the issue of input-to-state exponential stability (ISES) for stochastic complex-valued neural networks with neutral delay (SCVNNs) and discrete delay is considered. Without separating the SCVNNs into two real-valued systems, two criteria expressed through linear matrix inequality (LMI) to pledge ISES of the considered SCVNNs are derived based on It o formula in complex-valued field, Lyapunov-Krasovskii functional approach as well as some relevant inequality skills. Two examples are furnished to verify the raised results.

The analysis and synthesis issues have gained widespread attention for complex dynamical networks (CDNs) over the past few years. Accordingly, some challenges including protocol-based scheduling, s...

In this paper, the intermittent dynamic event-based state estimation problem is investigated for a class of delayed complex dynamic networks (CDNs). The estimate is implemented based on the measurements from a fraction of network nodes. In the framework of aperiodic intermittent measurement outputs, a dynamic event-triggered mechanism is introduced to save communication resources and reduce actuation burden. The aim of this work is to design a dynamic event-based state estimator by adopting intermittent dynamic event-triggered (IDET) strategy, such that the dynamics of estimation error system is exponentially stable. By resorting to Halanay inequality and switched system method, the sufficient conditions are derived for ensuring the existence of the desired state estimator, and are characterized by the ratio of the average working time to the average rest time. In the meanwhile, the estimator gains for partial nodes are explicitly obtained by solving some matrix inequalities. Furthermore, it is also proven that Zeno behavior can be excluded under the proposed IDET strategy. Finally, a numerical simulation is provided to verify the effectiveness of the derived results.

The stability of fractional-order complex-valued neural networks (FOCVNNs) with probabilistic time-varying delays is investigated in this paper. By constructing suitable Lyapunov–Krasovskii functional and utilizing inequality technique, a complex-valued linear matrix inequality (LMI) criterion guaranteeing the global asymptotic stability of the proposed FOCVNNs is deduced. A numerical example with simulations is provided to demonstrate the feasibility and availability of the obtained theoretical result.

This paper is devoted to researching the robust stability of fractional-order complex-valued projective neural networks (FOCVPNNs) with neutral-type delays and uncertain parameters. Without dividing the FOCVPNNs into two real-valued systems, based on Lyapunov method, matrix inequality technique and homeomorphism principle, several delay-independent and delay-dependent criteria are established to make sure that the equilibrium point of the addressed FOCVPNNs is existent, unique and globally robustly stable. Finally, three examples with simulations are given to verify the availability of the main results.

Nowadays, the Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integrated navigation system is widely used in many applications. The extended Kalman Filter (EKF) is a popula...

This paper is concerned with the l2–l∞ output-feedback control problem for a class of two-dimensional (2-D) switched systems described by the general Fornasini–Marchesini second model under the sto...

Copyright © 2021 The Author(s). In recent years, the communication-protocol-based synthesis and analysis issues have gained substantial research interest owing mainly to their significance in networked systems. In this work, we survey the control and filtering problems of networked systems under the effects induced by communication protocols. First, we introduce the engineering background of networked systems as well as the theoretical frameworks established to deal with the communication-protocol-based analysis and synthesis problems. Then, recent advances (especially the latest results) are reviewed on the stability analysis issue subject to protocol scheduling. Subsequently, the particular effort is devoted to presenting the latest progress on various communication-protocol-based control and filtering problems according to the characteristics of networked systems (e.g. time-varying nature, random behaviours, types of parameter uncertainties, and kinds of distributed structure). After that, we provide a systematic review of the communication-protocol-based fault diagnosis problems. Finally, some research challenges of communication-protocol-based control and filtering problems are outlined for future research.

In this paper, a competitive mechanism integrated whale optimization algorithm (CMWOA) is proposed to deal with multi-objective optimization problems. By introducing the novel competitive mechanism, a better leader can be generated for guiding the update of whale population, which benefits the convergence of the algorithm. It should also be highlighted that in the competitive mechanism, an improved calculation of crowding distance is adopted which substitutes traditional addition operation with multiplication operation, providing a more accurate depiction of population density. In addition, differential evolution (DE) is concatenated to diversify the population, and the key parameters of DE have been assigned different adjusting strategies to further enhance the overall performance. Proposed CMWOA is evaluated comprehensively on a series of benchmark functions with different shapes of true Pareto front. Results demonstrate that proposed CMWOA outperforms other three methods in most cases regarding several performance indicators. Particularly, influences of model parameters have also been discussed in detail. At last, proposed CMWOA is successfully applied to three real world problems, which further verifies the practicality of proposed algorithm.

This paper focuses on the stabilization of the T-S fuzzy fractional rectangular descriptor time-delay system. To convert the fuzzy rectangular time-delay system into the fuzzy square time-delay sys...

In this paper, the sampled-based consensus problem is investigated for a class of nonlinear multiagent systems subjected to deception attacks. Due to network fluctuations and limited resources, deception attacks might destroy the sampled-data in communication networks. Additionally, the success of deception attacks greatly depends on some randomly fluctuated factors. This paper takes into account the deception attacks that are randomly launched at each sampling instant. The eigenvector of Laplacian matrix is utilized to construct a novel Lyapunov functional. Then, the decoupled criterion connected with eigenvalues of Laplacian matrix is obtained such that the addressed multiagent systems achieve the mean square consensus. Furthermore, the solutions of a set of matrix inequalities represent the controller gain matrix. Finally, a numerical example is provided to validate the effectiveness of the derived results.

This paper focuses on the robust stability analysis of fractional-order quaternion-valued neural networks (FOQVNNs) with neutral delay and parameter uncertainties. Without transforming the FOQVNNs into equivalent two complex-valued systems or four real-valued systems, based on homeomorphism principle, matrix inequality technique and Lyapunov method, both delay-independent and delay-dependent criteria to guarantee the existence, uniqueness and global stability of equilibrium point for the considered FOQVNNs are derived in the form of linear matrix inequality (LMI). Two examples with simulations are provided to manifest the theoretical results.

The phenomena of unknown correlations are ubiquitously existing in general distributed filtering problems over sensor networks. And the covariance intersection (CI) fusion rule is an effective tool to tackle with this phenomena. During the recent years, the related CI-based Kalman consensus filters (CIKCFs) have been adopted to deal with unknown correlations in sensor networks. However, a systematic stability analysis result for the general CIKCF in the time-varying system setting remains to be established. This paper is written for this purpose. First, a general CIKCF with full features of CI is presented. Accordingly, the conditions for CIKCF to reach consensus with varying weights are investigated. Furthermore, a novel detectability condition, i.e., collectively uniform detectability , is proposed to ensure the error covariances of the CIKCF are uniformly bounded. Based on this condition, the estimation errors are further proven to be exponentially bounded in mean square with the aid of the stochastic stability lemma . Finally, an example is given to validate the effectiveness of the theoretical results.

Stability problem of quaternion-valued neutral-type delayed neural networks (QVNTDNNs) is researched in this paper. By utilizing homeomorphism principle, matrix inequality technique and Lyapunov method, both delay-independent and delay-dependent stability criteria are established in the form of linear matrix inequalities to pledge the existence, uniqueness and global stability of the equilibrium point of the considered QVNTDNNs. An example with simulations is given to manifest the validity of the exported results.

The asymptotic stability and synchronization for nonlinear distributed-order system with uncertain parameters is investigated in this paper. To this end, a new lemma for the distributed-order system is proposed to certify the asymptotic stability. Several sufficient conditions are presented to acquire the asymptotic stability and synchronization for such distributed-order models. In addition, by designing two numerical examples, the feasibility and effectiveness of theoretical results are verified.

In this paper, the robust stability of uncertain fractional order singular systems with neutral and time-varying delays is investigated. By applying Lyapunov–Krasovskii functional approach, several sufficient conditions in the form of linear matrix inequality to ensure asymptotical stability and robust stability are derived for the considered systems. The advantage of the employed method in this paper is that one may directly calculate integer-order derivatives of Lyapunov–Krasovskii functional. Several simple examples are given to illustrate the effectiveness of the obtained results.

In this paper, we are concerned with the problem of containment control for a class of nonlinear multi-agent system. The local dynamics under consideration is governed by a nonlinear system with ti...

In this paper, the quasi-consensus problem is investigated for a class of heterogeneous-switched nonlinear multiagent systems, in which both cooperation and competition interactions are considered simultaneously. By means of the Lyapunov function method, we show that quasi-consensus can be ensured for switched multiagent systems under the assumption that the activation time of cooperation interactions is sufficiently large. Moreover, a new Lyapunov function is considered to provide the lower and upper bounds of switching intervals explicitly. Thus, these bounds can be used to obtain less conservative stability results of switched systems. Furthermore, the established results are specialized to both the traditional consensus case and the stability of linear-switched systems. Finally, simulations are given to illustrate the theoretical results derived in this paper.

This paper gives a review of recent advances on memristive neural networks with emphasis on the issues of stability analysis and state estimation. First, the concept of memristive neural network is recalled with a brief introduction of its background. Then, certain types of frequently seen neural networks are reviewed comprehensively with latest progress. Some engineering-oriented phenomena that appear extensively in the context of networked systems are introduced and summarized, including random dynamics, time-delays and network-induced incomplete information, etc. From different perspectives, several techniques explored for designing the required state estimators of memristive neural networks are discussed in detail. Some latest progress regarding the stability analysis and state estimation problems for discrete time memristive neural networks are presented. Finally, we provide the conclusions and point out certain future research directions.

In this paper, the recursive estimation problem is investigated for the power quality disturbances. A system model for the power quality disturbances with multiplicative noises and correlated noise...

In this paper, the problem of event-triggered non-fragile state estimator design for discrete-time delayed neural networks (DNNs) is investigated over finite-time span. In consideration of the changes of environment and high sensitivity, external disturbances and/or parameter uncertainties might be involved in estimator parameters of the concerned DNNs. Therefore, it is one of our main objectives to design a non-fragile state estimator subject to the norm bounded gain variation. The sensor nonlinearity is supposed to occur in a random way. In the meanwhile, the event-triggered scheme and energy constraints are adopted in state estimator design for the purpose of energy and resource saving. By using the Lyapunov stability theory and some analytical techniques, sufficient conditions are established to guarantee that the estimation error system is finite-time bounded and meet a prescribed mixed H∞ and passivity performance constraint. Furthermore, the estimator gains are obtained via solving a set of linear matrix inequalities (LMIs). Finally, two numerical examples are exploited to demonstrate the effectiveness of the developed technique.

Results of laser-induced single event upset testing of devices with embedded error correction codes are described. Specifically, the memory system, consisting of a Micron MT41K256M16TW-107 IT: P 32 Meg×16×8 banks Dual Dynamic Random Access Memories (DDRs) was tested on a self-made test board. Two samples of each memory system or device type were irradiated with a pulsed laser. The units were irradiated by using a continual read/write correct loop in several bit patterns. Results for both correctable and uncorrectable errors are presented along with soft error data records.

In this paper, the periodicity of a class of impulsive Cohen–Grossberg-type fuzzy neural networks with hybrid delays is investigated. By applying integro-differential inequality with delays, the Lyapunov function, the Poincare map and the fixed point theory, we obtained some new sufficient conditions which guarantees the existence of periodic solutions and global exponential stability of the formulated system. Three numerical examples are given to show the effectiveness and generality of our method.

In this paper, the state estimation problem is investigated for a class of continuous-time complex networks with bounded distributed delay. For the network under consideration, only the outputs from a fraction of nodes are available and this put forward the new challenge, that is, the so-called partial-nodes-based state estimation problem. In order to reduce the usage of the communication resources, a general event-triggering rule is considered in the design of the estimator. A novel estimator is constructed and, by constructing novel Lyapunov-Krasovskii functionals, some easy-to-check conditions are derived such that the error dynamics is exponentially ultimately bounded. Furthermore, it is shown that the Zeno behavior can be excluded from the event-triggering rules. Numerical simulations are presented to further illustrate the effectiveness of the theoretical results.

This paper focus on the synchronization of complex-valued neural networks (CVNNs) with both discrete and distributed two additive time-varying delays. By applying matrix inequality technique and exploiting reciprocally convex approach, several delay-dependent criteria are presented in the form of linear matrix inequalities (LMIs) to ensure the global synchronization of CVNNs via structuring an appropriate Lyapunov–Krasovskii functional. An example with simulations is provided to ensure the feasibility of the obtained result.

This paper is concerned with the distributed ${H_\infty }$ state estimation for a discrete-time target linear system over a filtering network with time-varying and switching topology and partial information exchange. Both filtering network topology switching and partial information exchange between filters are simultaneously considered in the filter design. The topology under consideration evolves not only over time but also by an event switch which is assumed to be subject to a nonhomogeneous Markov chain . The probability transition matrix of the nonhomogeneous Markov chain is time-varying . In the filter information exchange, partial state estimation information and channel noise are simultaneously considered. In order to design such a switching filtering network with partial information exchange, stochastic Markov stability theory is developed. The switching topology-dependent filters are derived to guarantee an optimal ${H_{\infty }}$ disturbance rejection attenuation level for the estimation disagreement of the filtering network. It is shown that the addressed ${H_\infty }$ state estimation problem is turned into a switching topology-dependent optimal problem. The distributed filtering problem with complete information exchanges from its neighbors is also investigated. An illustrative example is given to show the applicability of the obtained results.

This paper deals with the global exponential stability in Lagrange sense for quaternion-valued neural networks (QVNNs) with leakage delay, discrete time-varying delays and distributed delays. By st...

This paper investigates the recursive state estimation problem for a class of discrete-time stochastic complex networks with packet disorders under Round-Robin (RR) communication protocols. The phenomenon of packet disorders results from the random transmission delays during the signal propagation process due to the unpredictable fluctuations of the network load, and such random delays are modeled by a set of random variables satisfying certain known probability distributions. For the sake of lessening the communication burden and abating the data collisions, the RR protocol is introduced to govern the order of the nodes for data transmission. Under the scheduling of the RR protocol, only one node is allowed to gain the access to the network at each time instant. Then, a recursive estimator is devised to guarantee an upper bound for the estimation error covariance, and then the obtained upper bound is locally minimized by adequately choosing the estimator parameters. Furthermore, the boundedness of estimation error is analyzed in the sense of mean square with the help of stochastic analysis techniques. At last, a simulation example is presented to show the applicability of the proposed estimator design scheme. 10.13039/501100004054-King Abdulaziz University (Grant Number: RG-19-611-42); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61773017, 61873148, 61873230 and 61933007); Royal Society of the U.K.; Alexander Von Humboldt Foundation of Germany

The periodicity problem of a class of Cohen–Grossberg-type fuzzy neural networks with impulses and time-varying delays is concerned in this paper. Via constructing a delay differential inequality, and applying fuzzy theory and the Lyapunov method, several criteria which ensure the existence and exponential stability of the periodic solutions for the considered systems are derived. An example is shown to illustrate the validity of the obtained results.

This paper is concerned with the open-circuit fault diagnosis of phase-controlled three-phase full-bridge rectifier by using a sparse autoencoder-based deep neural network (SAE-based DNN). Firstly, some preliminaries on SAE-based DNN are briefly introduced to automatically learn the representative fault features from the raw fault signals. Then, a novel strategy is developed to design the structure of the SAE-based DNN, by which the depth and hidden neurons of the SAE-based DNN could be regularly determined to extract the features of input signals. Furthermore, the fault model and system framework are presented to diagnose the open-circuit fault of the three-phase full-bridge rectifier. Finally, the effectiveness of the developed novel strategy is verified by the results of simulation experiments, and the superiority of the novel SAE-based DNN is evaluated by comparing with other frequently used approaches.

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.

In this paper, the problem on synchronization is investigated for a class of impulsive complex-valued neural networks with discrete and distributed time-varying delays as well as leakage delay. By constructing appropriate Lyapunov–Krasovskii functional, and using Newton–Leibniz formulation, inequality technique and free-weighting matrix method, several sufficient criteria to guarantee the global μ-synchronization are derived for the considered impulsive complex-valued neural networks. The provided conditions are expressed in terms of linear matrix inequalities, and are dependent on the sizes of discrete delays, distributed delays and leakage delay. An example with simulations is provided to verify the effectiveness of the obtained results.