Search

In this paper, a robust H ∞ design approach is proposed to accomplish the state estimation task for a class of continuous-time gene regulatory networks (GRNs) in the finite-time framework. For the GRNs under investigation, the distributed inter-node time delays and intra-node time delays are, respectively, taken into account in the feedback regulation function and the translation process. Moreover, the parameter uncertainty with convex polytopic constraint is introduced to characterize the unavoidable modeling errors induced by parameter sensitivity, calibration accuracy, abrupt structural changes and failures, etc. The primary objective of this paper is to design a robust state estimator for the considered GRNs based on the measured concentrations of messenger ribonucleic acid and protein such that the finite-time boundedness and the H ∞ performance are guaranteed. By resorting to the Lyapunov stability theory, the Jensen's inequality and the Gronwall inequality, some sufficient conditions are established in terms of linear matrix inequalities. Subsequently, a set of parameter matrices with special structures is designed by means of the convex optimization techniques. Finally, a numerical example is provided to demonstrate the finite-time boundedness and the robust performance of the developed state estimation approach. [ABSTRACT FROM AUTHOR]

We investigate the remote state estimation problem for networked systems over parallel noise-free communication channels. Due to limited network capabilities in practical network environments, communication schedulers are implemented at the transmit side of each subchannel to promote resource efficiency. Specifically, the processed signals are transmitted only when it is necessary to provide the real-time measurements to the remote estimator. The recursive approximate minimum mean-square error estimator is established to restore the state vector of a target plant by utilizing the scheduled transmission signals. All the information coming from the individual subchannels, even if no measurement is sent, will contribute to improve the estimation performance in an analytical form. Finally, a numerical example is given to illustrate the effectiveness of the main results. • The remote state estimation problem is studied for networked systems. • Parallel noise-free communication channels are considered. • Communication schedulers are implemented at the transmit side. • Recursive approximate minimum mean-square error estimator is established. • All sub-channels contribute to improve the estimation performance. [ABSTRACT FROM AUTHOR]

This paper is concerned with the state estimation problem for delayed complex dynamic networks with non-identical local dynamical systems. The state estimation is conducted based on constrained information of the measurement outputs. Specifically, the network outputs are available only from a portion of network nodes, and such outputs are transmitted from the network nodes to the estimator in an intermittent way. By utilizing the Halanay inequality method as well as the average dwell-time approach, two sets of sufficient conditions are established that ensure the error dynamics of the state estimation to converge to zero exponentially, and explicit expressions of the estimator gains are further characterized. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approaches. • The state estimation problem is considered for delayed complex networks. • Partially available network measurements are used. • The network output is transmitted to the estimator in an intermittent way. • The Halanay inequality method and average dwell-time approach are used. • Explicit expressions of the estimator gains are further characterized. [ABSTRACT FROM AUTHOR]

This paper studies a class of cooperative games, called graphical cooperative games, where the internal topology of the coalition depends on a prescribed communication graph among players. First, using the semitensor product of matrices, the value function of graphical cooperative games can be expressed as a pseudo-Boolean function. Then, a simple matrix formula is provided to calculate the Shapley value of graphical cooperative games. Finally, some practical examples are presented to illustrate the application of graphical cooperative games in communication-based coalitions and establish the significance of the Shapley value in different communication networks. [ABSTRACT FROM AUTHOR]

This paper is concerned with the finite-time state estimation problem for a class of delayed artificial neural networks under the stochastic communication protocol. The underlying time delay is time-varying yet bounded. Compared with the common-used sigmoid-type nonlinearity, a more general type of nonlinearity is adopted to describe the neuron activation function and the nonlinearity of the measurement output, respectively. In order to avoid the communication collision, the stochastic communication protocol is introduced between the transmitter and the receiver, and the corresponding scheme is characterised with the help of a Markov chain. By introducing an auxiliary vector, a novel state estimator structure is proposed. The stochastic finite-time stability of the error dynamics is first analyzed via the stochastic analysis techniques and the Lyapunov stability theory, and then the sufficient condition for the existence of the desired state estimator is obtained. Subsequently, the estimator gain is parameterized by using a set of easy-to-check computational condition. Finally, a numerical example is provided to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple attribute decision-making problems with picture 2-tuple linguistic information. Then, we utilize arithmetic and geometric operations to develop several picture 2-tuple linguistic aggregation operators. The prominent characteristic of these proposed operators is studied. Then, we have utilized these operators to develop some approaches to solving the picture 2-tuple linguistic multiple attribute decision-making problems. Finally, a practical example for enterprise resource planning (ERP) system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple attribute decision making problems based on the aggregation operators with hesitant bipolar fuzzy information. Then, motivated by the ideal of arithmetic and geometric operation, we have developed some aggregation operators for aggregating hesitant bipolar fuzzy information: hesitant bipolar fuzzy weighted average (HBFWA) operator, hesitant bipolar fuzzy weighted geometric (HBFWG) operator, hesitant bipolar fuzzy ordered weighted average (HBFOWA) operator, hesitant bipolar fuzzy ordered weighted geometric (HBFOWG) operator, hesitant bipolar fuzzy hybrid average (HBFHA) operator and hesitant bipolar fuzzy hybrid geometric (HBFHG) operator. Then, we have utilized these operators to develop some approaches to solve the hesitant bipolar fuzzy multiple attribute decision making problems. Finally, an illustrative example for evaluating the constructional engineering software quality is then analyzed to illustrate the relevance and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple attribute decision making (MADM) problem based on the arithmetic and geometric aggregation operators with hesitant fuzzy linguistic information. Then, motivated by the idea of traditional arithmetic operation, we have developed some aggregation operators for aggregating hesitant fuzzy linguistic information: hesitant fuzzy linguistic weighted average (HFLWA) operator, hesitant fuzzy linguistic ordered weighted average (HFLOWA) operator and hesitant fuzzy linguistic hybrid average (HFLHA) operator. Furthermore, we propose the concept of the dual hesitant fuzzy linguistic set and develop some aggregation operators with dual hesitant fuzzy linguistic information. Then, we have utilized these operators to develop some approaches to solve the hesitant fuzzy linguistic multiple attribute decision making problems. Finally, a practical example is given to verify the developed approach and to demonstrate its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

Optical wireless systems are promising candidates for next-generation indoor communication networks. Optical wireless technology offers freedom from spectrum regulations and, compared to current radio-frequency networks, higher data rates and increased security. This paper presents a fast adaptation method for multibeam angle and delay adaptation systems and a new spot-diffusing geometry, and also considers restrictions needed for complying with eye safety regulations. The fast adaptation algorithm reduces the computational load required to reconfigure the transmitter in the case of transmitter and/or receiver mobility. The beam clustering approach enables the transmitter to assign power to spots within the pixel's field of view (FOV) and increases the number of such spots. Thus, if the power per spot is restricted to comply with eye safety standards, the new approach, in which more spots are visible within the FOV of the pixel, leads to enhanced signal-to-noise ratio (SNR). Simulation results demonstrate that the techniques proposed in this paper lead to SNR improvements that enable reliable operation at data rates as high as 15 Gbit/s. These results are based on simulation and not on actual measurements or experiments. [ABSTRACT FROM AUTHOR]

In this paper, a general framework of user-based collaborative filtering (CF) is developed with a new p -moment-based similarity measure. The p -moment-based statistics (PMS) of individual rating data are employed to analyze the user rating habit, thereby facilitating the performance improvement of the CF algorithm. On the basis of the PMS, a new yet comprehensive similarity measure is proposed to quantify the distance between two users with focus on both the users' preferences on items and their rating habits. Compared with the traditional ones, our proposed similarity measure is more general with clearer application insights in complicated situations. Furthermore, the weights of different statistics are regarded as adjustable parameters that are determined by utilizing the particle swarm optimization technique so as to achieve good prediction performance. Based on the proposed similarity measure with optimized weights, the neighborhood set consisting of similar users is formed and then the user-based rating prediction is eventually provided. The developed CF algorithm has advantages of high prediction accuracy and wide application potential. Moreover, this CF algorithm is applied on a real-world disease (Friedreich's ataxia) assessment system in order to assist diagnosis for patients with uncertain/missing information. Experimental results demonstrate the validity and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the robustfiltering problem for a class of nonlinear discrete time-delay stochastic systems. The system under consideration involves parameter uncertainties, stochastic disturbances, time-varying delays and sector nonlinearities. Both missing measurements and randomly occurring nonlinearities are described via the binary switching sequences satisfying a conditional probability distribution, and the nonlinearities are assumed to be sector bounded. The problem addressed is the design of a full-order filter such that, for all admissible uncertainties, nonlinearities and time-delays, the dynamics of the filtering error is constrained to be robustly exponentially stable in the mean square, and a prescribeddisturbance rejection attenuation level is also guaranteed. By using the Lyapunov stability theory and some new techniques, sufficient conditions are first established to ensure the existence of the desired filtering parameters. Then, the explicit expression of the desired filter gains is described in terms of the solution to a linear matrix inequality. Finally, a numerical example is exploited to show the usefulness of the results derived. [ABSTRACT FROM AUTHOR]

In this paper, we consider the robust H∞ control problem for a class of discrete time-delay stochastic systems with randomly occurring nonlinearities. The parameter uncertainties enter all the system matrices; the stochastic disturbances are both state and control dependent, and the randomly occurring nonlinearities obey the sector boundedness conditions. The purpose of the problem addressed is to design a state feedback controller such that, for all admissible uncertainties, nonlinearities, and time delays, the closed-loop system is robustly asymptotically stable in the mean square, and a prescribed H∞ disturbance rejection attenuation level is also guaranteed. By using the Lyapunov stability theory and stochastic analysis tools, a linear matrix inequality (LMI) approach is developed to derive sufficient conditions ensuring the existence of the desired controllers, where the conditions are dependent on the lower and upper bounds of the time-varying delays. The explicit parameterization of the desired controller gains is also given. Finally, a numerical example is exploited to show the usefulness of the results obtained. [ABSTRACT FROM AUTHOR]

In this paper, a novel beam power adaptation method is proposed, studied and shown to be a desirable means for improving the performance of an optical wireless (OW) system that operates under the constraints of background noise, multipath dispersion, and mobility. We propose and evaluate a new OW configuration that employs an adaptive beam clustering method (ABCM) in conjunction with diversity detection. Our goal is to reduce the effect of transmitter/receiver mobility and the associated impacts in terms of a weak received optical power and reduction in bandwidth. Previous work has shown that multiple spot diffusing techniques suffer from these two fundamental limitations associated with mobility. Our new ABCM can help overcome the impairments introduced by mobility, introduce gain in the received optical power, and increase bandwidth even at large transmitter and receiver separations. Our results indicate that, at the least successful locations, the ABCM system can reduce the signal delay spread by nearly a factor of twenty and enhance the SNR by almost 15 dB over a line strip multibeam system (LSMS). We also incorporate the concept of relaying into the Adaptive line strip multibeam system and prove that this technique can lead to considerable performance improvements. [ABSTRACT FROM AUTHOR]

In this article, the dynamic output‐feedback H∞ control problem is investigated for discrete‐time state‐delay systems subject to exogenous disturbances and actuator saturations under the round‐robin communication protocol. Using the switched system approach, the Lyapunov–Krasovskii functional and the modified sector condition, sufficient conditions are first obtained under which the closed‐loop systems can achieve some desirable performance indices such as the boundedness, the H∞ performance and the stability in the absence of disturbances. Then, the controller is explicitly characterized by means of the solvability of linear matrix inequalities. For the case without time delays, the corresponding conditions are also presented. Subsequently, the optimization problems about the performance indices are discussed. Finally, the benefit and effectiveness of the proposed results are specifically illustrated via two simulation examples. [ABSTRACT FROM AUTHOR]

In this article, we extend multi-attributive border approximation area comparison (MABAC) approach to the multiple attribute group decision making with picture 2-tuple linguistic numbers. We review the concept of picture 2-tuple linguistic sets and introduce its corresponding score function, accuracy function, and operational laws. In addition, we propose two aggregation operators of picture 2-tuple linguistic numbers and then develop a method by combining traditional MABAC model with the overall picture 2-tuple linguistic evaluation information. Our proposed method is increasingly accurate and valid even when the conflicting attributes are considered. We also provide a numerical instance for assessing and selecting the renewable energy power generation project to demonstrate the efficacy of our novel model. Finally, we compare our proposed approach with other traditional operators to further show its benefits. [ABSTRACT FROM AUTHOR]

This paper is concerned with the fault detection problem for a class of networked multi-rate systems with nonuniform sampling and dynamic quantization. The sampling interval of the measurements is allowed to be nonuniform that is governed by a time-homogenous Markov process with partly unknown and uncertain transition probabilities. The measured output is quantized by a dynamic quantizer and then transmitted through communication network subject to data missing. The main purpose of the problem under consideration is to design sampling-interval-dependent fault detection filters such that, in the simultaneous presence of nonuniform sampling, dynamic quantization, intermittent faults as well as missing measurements, the robustness of residuals with respect to the disturbance and the sensitivity of the residuals against the fault are guaranteed. Finally, a three-tank system is utilized to illustrate the effectiveness of the proposed fault detection scheme. [ABSTRACT FROM AUTHOR]

Summary: This paper considers the problem of adaptive fuzzy output‐feedback tracking control for a class of switched stochastic nonlinear systems in pure‐feedback form. Unknown nonlinear functions and unmeasurable states are taken into account. Fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy observer is designed to estimate the immeasurable states. Based on these methods, an adaptive fuzzy output‐feedback control scheme is developed by combining the backstepping recursive design technique and the common Lyapunov function approach. It is shown that all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded in mean square in the sense of probability, and the observer errors and tracking errors can be regulated to a small neighborhood of the origin by choosing appropriate parameters. Finally, a simulation result is provided to show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

Summary: This work studies the issue of synchronization control for a type of fractional‐order complex networks, in which the adaptive coupling matrix is considered under the directed topology structure. A pinning control strategy, with the free selection of pinning nodes, is adopted for the synchronization goal. Then, by absorbing the information of eigenvectors and adaptive laws for the coupling matrix, a new Lyapunov function is constructed, by which, and with the assistance of Gronwall inequality and network features, the sufficient condition for Mittag‐Leffler synchronization of the fractional‐order network is established. Accordingly, an easy verifiable algebraic criterion is further derived by means of some matrix inequalities. Besides, we also discuss the effect of outer coupling strength on the achievement of network synchronization. Finally, a numerical experiment is performed to show the evidence of the correctness and effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This paper proposes a matrix-based approach to investigate the controllability, reachability, and stabilizability of probabilistic finite automata (PFA). Firstly, the state transition probabilistic structure matrix is constructed for PFA, based on which a kind of controllability matrix is defined for PFA. Secondly, some necessary and sufficient conditions are presented for the controllability, reachability, and stabilizability of PFA with positive probability by using the controllability matrix. Finally, an illustrate example is given to validate the obtained new results. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the algebraic formulation, topological structure and set stability of Boolean networks with state-dependent delay (SDD). Firstly, using the algebraic state space representation (ASSR) method, the dynamics of Boolean networks with SDD is converted into an equivalent augmented system. Secondly, based on the equivalent augmented system, some necessary and sufficient conditions are presented to calculate fixed points and cycles of Boolean networks with SDD. Thirdly, it is proved that the set stability of Boolean networks with SDD is equivalent to the set stability of augmented system, and a necessary and sufficient condition is presented by constructing a kind of set stability matrix. Finally, the obtained results are applied to the strategy consensus of networked evolutionary games with memories. [ABSTRACT FROM AUTHOR]

This study considers the parameter identification problem of a pseudo‐linear autoregressive moving average system (i.e. linear‐in‐parameter autoregressive output‐error ARMA systems), whose disturbance is an ARMA process. By means of the filtering technique, a filtering‐based gradient iterative (F‐GI) algorithm and a filtering‐based least squares iterative (LSI) algorithms are presented for enhancing the estimation accuracy. Furthermore, a filtering‐based decomposition LSI algorithm is derived for improving the computational efficiency. The key is to use the hierarchical identification principle, to apply the data filtering technique for identification, and to replace the unknown terms in the information vectors with their estimates. Compared with the F‐GI algorithm, the filtering‐based LSI algorithm and the filtering‐based decomposition LSI algorithm have faster convergence rates. The simulation results indicate that the proposed algorithms are effective. [ABSTRACT FROM AUTHOR]

This paper is concerned with the recursive filtering problem for a class of time-varying nonlinear stochastic systems in the presence of event-triggered transmissions and multiple missing measurements with uncertain missing probabilities. The measurements from different sensors may undergo the missing phenomena, which are characterized by a set of mutually independent Bernoulli random variables and the missing probabilities could be uncertain. In addition, the event-triggered transmission mechanism is introduced to reduce the network communication burden, where the current measurement is transmitted to the remote filter only when it changes greatly compared with the previous one. The aim of this paper is to design a time-varying filter such that, in the presence of the multiple missing measurements, event-triggered transmission mechanism and stochastic nonlinearities, an upper bound of the filtering error covariance is obtained and then minimized by properly designing the filter gain. The explicit form of the filter gain is given in terms of the solutions to two recursive matrix equations. It is shown that the developed filtering scheme is of a recursive form applicable for the online computations. Finally, we provide two illustrative examples to demonstrate the feasibility and applicability of the developed event-triggered filtering scheme. [ABSTRACT FROM AUTHOR]

Bipolar 2-tuple linguistic aggregation operators are developed in the current study for aggregation operations including weighted average, weighted geometric average, ordered weighted average, ordered weighted geometric average, hybrid average and hybrid geometric average. We investigate the characteristics of these operators, and present methods that apply these operators to solving the bipolar 2-tuple linguistic multiple attribute decision making problems. A real-world example is then analyzed to illustrate the relevance and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Hamacher product is a t-norm and Hamacher sum is a t-conorm. They are good alternatives to algebraic product and algebraic sum, respectively. Nevertheless, it seems that most of the existing hesitant fuzzy aggregation operators are based on the algebraic operations. In this paper, we utilize Hamacher operations to develop some hesitant Pythagorean fuzzy aggregation operators: hesitant Pythagorean fuzzy Hamacher weighted average (HPFHWA) operator, hesitant Pythagorean fuzzy Hamacher weighted geometric (HPFHWG) operator, hesitant Pythagorean fuzzy Hamacher ordered weighted average (HPFHOWA) operator, hesitant Pythagorean fuzzy Hamacher ordered weighted geometric (HPFHOWG) operator, hesitant Pythagorean fuzzy Hamacher hybrid average (HPFHHA) operator and hesitant Pythagorean fuzzy Hamacher hybrid geometric (HPFHHG) operator. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the hesitant Pythagorean fuzzy multiple attribute decision making problems. Finally, a practical example is given to verify the developed approach and to demonstrate its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple attribute decision making problems with Pythagorean 2-tuple linguistic information. Then, we utilize arithmetic and geometric operations to develop some Pythagorean 2-tuple linguistic aggregation operators. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the Pythagorean 2-tuple linguistic MADM problems. Finally, a practical example for enterprise resource planning (ERP) system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

Using the approach of semi‐tensor product of matrices, this study studies a class of event‐triggered control for finite evolutionary networked games, where the control only works at some certain individual states. First, by identifying 'control does not work' as a new specific control strategy, the controlled game dynamics is converted into an algebraic form. Second, to make the game converge globally, two necessary and sufficient conditions for the existence of event‐triggered control are obtained. Meanwhile, a constructive procedure is proposed to design state feedback control strategy and an adjustment method is presented to minimise the control times. Finally, the developed theory results are illustrated by a numerical method. [ABSTRACT FROM AUTHOR]

In this paper, the fusion estimation problem is investigated for a class of multi-sensor networked systems with unknown-but-bounded noises and mixed time-delays under try-once-discard protocols (TODPs). The measurements of multiple sensor nodes are sent to the remote fusion center through individual network channels, where the TODP is implemented in each channel to schedule the signal transmissions for the purpose of avoiding data collisions. To fuse the information collected from all channels, a sequential estimator is proposed whose estimator parameters are designed such that the estimation error (after each measurement update) is restrained into a zonotope with minimum F -radius at each moment. First, by using the properties of zonotopes, desired zonotopes are obtained that contain the estimation errors, and the F -radii of these zonotopes are subsequently minimized by appropriately designing the estimator parameters. It is shown that, under the proposed zonotopes-based fusion rule, the designed sequential estimator leads to the same estimation accuracy as that of the widely utilized parallel estimator. Finally, an illustrative example is provided to verify the validity of the developed fusion estimation method. • The fusion estimation problem is investigated for multi-sensor networked systems. • Mixed time-delays under try-once-discard protocols are considered. • A sequential estimator is proposed by using the properties of zonotopes. • The desired zonotopes are obtained that contain the estimation errors. [ABSTRACT FROM AUTHOR]

In this paper, a novel dual-pathway-fusion-based sequence-to-sequence learning model (DPF-S2S) is proposed for text recognition in the wild, which mainly focuses on enriching the spatial information and extracting high-dimensional representation features to assist decoding. In particular, a double alignment module is developed to solve the problem of text misalignment, where both position and vision information are well considered. Moreover, a global fusion module is deployed to enrich 2D information in the aligned attention maps, which benefits accurate recognition from complicated scenes with arbitrary text shapes and poor imaging conditions. Benchmark evaluations on seven datasets have demonstrated the superiority of proposed DPF-S2S model in comparison to other state-of-the-art text recognition methods, which presents great competitiveness on identifying texts in both regular and irregular scenes. In addition, extensive ablation studies have been carried out, which validate the effectiveness of applied strategies in proposed DPF-S2S. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stability issue of quaternion-valued neural networks with neutral delay, proportional delay and leakage delay. Taking use of the principle of homeomorphism, techniques of matrix inequality and Lyapunov stability theory, a main stability criterion is derived in the form of quaternion-valued linear matrix inequality for ensuring the unique existence and global stability of the equilibrium point for the considered quaternion-valued neural networks. An illustrative example and its simulations are given to show the effectiveness of the theoretical result. [ABSTRACT FROM AUTHOR]

As an effective vehicle, uncertainty theory is applicable for handling subjective indeterminacy. Based on uncertainty theory, the Hurwicz model of the zero-sum uncertain differential game with jump is formulated, in which the dynamic system is portrayed by an uncertain differential equation satisfying both the canonical Liu process and V-jump uncertain process. An equilibrium equation for solving the saddle-point of the above game is proposed. In addition, the game with a linear dynamic system and the quadratic objective function is further analysed. At last, a resource extraction problem using our theoretical results is described. [ABSTRACT FROM AUTHOR]

Lateral flow immunoassay (LFIA), as a well-known point-of-care testing (POCT) technique, is of vital significance in a variety of application scenarios due to the advantages of convenience and high efficiency. With rapid development of computational intelligence (CI), algorithms have played an important role in enhancing LFIA performance, and it is necessary to summary how algorithms can assist LFIA improvement for providing experiences. However, most existing works on LFIA are from biochemical field which pay more attention to material and reagent. Therefore, in this paper, a systematical survey is proposed to review works on applying mathematical tools to promote LFIA development. Particularly, a novel two-level taxonomy is designed for a better inspection, including LFIA-oriented mathematical modelling, CI-assisted post-processing and quantification in LFIA, and each level is further subdivided for in-depth understanding. In addition, from a higher viewpoint, outlooks of jointly developing POCT with other state-of-the-art techniques are presented from perspectives of implementation principle, technical approach and algorithm application. Moreover, this survey aims to highlight that applying CI methods is competent for boosting POCT development, so as to raise attentions from more areas like information science, extend deeper researches and inspire more interdisciplinary works. [ABSTRACT FROM AUTHOR]

This paper deals with the state estimate problem for a class of discrete-time complex networks. The target plant involves Markovian jumping parameters and mode-dependent distributed time-delays. It is assumed that the measurements for the estimate purpose are just from a portion of network nodes, and corrupted by a certain stochastic noise. A unified framework is developed to cope with the state estimation issue based on the outputs from the fraction of nodes for the discrete-time complex networks with distributed time delay and measurement noise. By employing the Lyapunov stability theory and some new techniques, the sufficient conditions are established to ensure the state estimation error is exponentially ultimately mean-square bounded. As the special case when the measurement is noise-free, the resulting criteria are to guarantee that the dynamics of state estimation error is exponentially stable. In addition, the estimator gain matrices are given in the explicit formula. Finally, numerical simulations are presented to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-triggered H ∞ fusion estimation problem for uncertain discrete-time Hamiltonian systems with time-varying delays and sensor saturations. In order to improve the efficiency of resource utilization, a component-based event-triggered mechanism is implemented on the communication path from sensors to the estimator, where each output is sent only when a prescribed event-triggering condition is satisfied. By considering the structural characteristics of discrete-time Hamiltonian systems and utilizing the Lyapunov–Krasovskii stability, sufficient conditions are established to guarantee the augmented system (i.e., the underlying system combined with the estimation error dynamics) is locally exponentially stable. Meanwhile, the estimator gain is acquired by resorting the approach of certain matrix inequality. Finally, two examples are provided to demonstrate the effectiveness of the proposed scheme. • The event-triggered fusion estimation problem is considered. • Uncertain discrete-time Hamiltonian systems are dealt with. • Both time-varying delays and sensor saturations are investigated. • A component-based event-triggered mechanism is implemented. • Sufficient conditions are established for the stability of the estimation errors. [ABSTRACT FROM AUTHOR]

This paper is concerned with the dynamic event-triggered state estimation problem for a class of spatial-temporal networks (STNs) with time-varying delays under an encoding-decoding strategy. For the sake of reducing the unnecessary resources wastes, we establish a dynamic event-triggered mechanism to determine whether the current measurement output data is transmitted to the filter, where the threshold is dynamically adjusted according to a certain rule. In order to enhance the robustness of signal transmission, an encoding-decoding strategy is exploited in the process of the data transmission. To be specific, the original signals encoded as a bit string are transmitted through binary symmetric channels with certain crossover probabilities and then restored by a decoder at the receiver. By constructing Lyapunov-Krasovskii functional, we obtain a sufficient condition to ensure that the estimation error system is exponential mean square ultimately bounded. Subsequently, the desired state estimator is designed in terms of the solution to a certain matrix inequality. Finally, a numerical example is shown to demonstrate that the proposed state estimator is valid for time-delayed STNs. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the problem of analysis and synthesis for a class of output feedback control system. The system under consideration is a discrete-time stochastic system with time-varying delay. It is assumed that the measurement of system is quantized via a logarithmic quantizer before it is transmitted, and the measurement data would be missing from time to time which can be described by a Bernoulli distributed white sequence. In addition, the nonlinearities are assumed to satisfy the sector conditions. The problem addressed is to design an output feedback controller such that the resulting closed-loop system is exponentially stable in the mean square. By employing Lyapunov theory and some new techniques, a new framework is established to cope with the design of output feedback controller for nonlinear systems involving quantization and missing measurement. Sufficient conditions are derived to guarantee the existence of the desired controllers, and the controller parameters are given in an explicit expression as well. A numerical example is exploited to show the usefulness of the results obtained. [ABSTRACT FROM AUTHOR]

How to select the optimisation criterion of the performance function is a pivotal research topic for the uncertain differential game. The Hurwicz criterion is a flexible optimisation measure to allocate some weight between optimism and pessimism to achieve balance for both of them. Based on uncertainty theory, this paper considers the Nash equilibrium and bang-bang property for the multi-player non-zero-sum uncertain differential game model by applying the Hurwicz criterion to optimise the performance function. To coordinate onto the Nash equilibrium, an equilibrium equation is derived. In addition, the application of bang-bang optimal control in the multi-player game problem of the uncertain linear system is investigated. Finally, an example is shown to validate the results obtained. [ABSTRACT FROM AUTHOR]

This paper is devoted to analysing the global Mittag-Leffler stability of fractional-order quaternion-valued neural networks with piecewise constant arguments and impulses. The quaternion direct method is adopted to address the considered model avoiding any decomposition. By utilising the matrix inequality technique and Lyapunov direct method, some sufficient conditions to guarantee the global Mittag-Leffler stability of equilibrium point for the considered model are obtained in the form of quaternion-valued linear matrix inequalities (LMIs). To certify the validity of the derived result, a numerical example is presented. [ABSTRACT FROM AUTHOR]

Uncertainty theory is a new branch of mathematics concerned with the analysis of subjective indeterminacy. Based on the concept of uncertain theory, this paper considers optimal control and zero-sum differential game for multi-factor uncertain continuous-time singular systems with Hurwicz criterion, where dynamic systems are modelled by a type of uncertain differential equation driven by canonical Liu process. Using the approach of dynamic programming, the principle of optimality is proposed and then the equation of optimality is derived to resolve an uncertain control model. A two-player zero-sum uncertain differential game is further investigated based on the optimality equation above, and an equilibrium equation is presented to locate a saddle-point in the game. Finally, an example is discussed to illuminate the effectiveness of the results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the receding horizon filtering problem for a class of discrete time-varying nonlinear systems with multiple missing measurements. The phenomenon of missing measurements occurs in a random way and the missing probability is governed by a set of stochastic variables obeying the given Bernoulli distribution. By exploiting the projection theory combined with stochastic analysis techniques, a Kalman-type receding horizon filter is put forward to facilitate the online applications. Furthermore, by utilizing the conditional expectation, a novel estimation scheme of state covariance matrices is proposed to guarantee the implementation of the filtering algorithm. Finally, a simulation example is provided to illustrate the effectiveness of the established filtering scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-triggered distributed state estimation problem for a class of uncertain stochastic systems with state-dependent noises and randomly occurring uncertainties over sensor networks. An event-triggered communication scheme is proposed in order to determine whether the measurements on each sensor should be transmitted to the estimators or not. The norm-bounded uncertainty enters into the system in a random way. Through available output measurements from not only the individual sensor but also its neighbouring sensors, a sufficient condition is established for the desired distributed estimator to ensure that the estimation error dynamics are exponentially mean-square stable. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities, and then the explicit expression is given for the distributed estimator gains. Finally, a simulation example is provided to show the effectiveness of the proposed event-triggered distributed state estimation scheme. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic event‐based recursive filtering problem is studied for multirate systems over sensor networks. The state update rate of the plant and the sampling rate of the sensors are allowed to be different in order to reflect the multirate sampling strategy. Moreover, the phenomenon of integral measurements is considered to cater for the real engineering practice. To reduce unnecessary data transmissions, the dynamic event‐based mechanism is implemented in the communication channels among sensor nodes. The purpose of this paper is to design a distributed recursive filtering scheme such that, under the influence of the integral measurements, the multi‐rate sampling, and the dynamic event‐based mechanism, there exists a minimal upper bound on the filtering error covariance. An upper bound on the filtering error covariance is first derived by solving a matrix Riccati equation, and then minimized at each sampling instant by choosing appropriate filter gains. Comprehensive simulations are conducted on a numerical example and a practical example to show the effectiveness and superiority of the proposed dynamic event‐based recursive filtering scheme. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 Itô 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. [ABSTRACT FROM AUTHOR]