Search

• The paper is the first batch that tries to inquiry the GPS of the PDIMNNs. Furthermore, unlike the previous literature [11,21,35] , the nonlinear substitution that converts the PDs system to a constant delays system is not used. Because the subsequent pro-cessing of the converted system is complicated, this paper directly performs on the original PDs system. • The paper employs the non-reduced-order method, which r-efrains the double-dimensional problem after reduced-order [3–5,23,24]. In practical applications, the method is more ponderable and si-gnificative for second-order scheme under controller. • In the paper, devise both the feedback controller and the adaptive controller for the first time to achieve the GPS of the PD-IMNNs. The advantages of the two types of controllers are compar-ed through numerical examples and simulations. The mathematical model is closer to reality, and selecting the appropriate controller in application can further reduce control expenses. This article probes into the global polynomial stabilization (GPS) of proportional delayed inertial memristive neural networks (PDIMNNs). Here, ruling out the reduced-order way, discuss the GPS of PDIMNNs under the second-order scheme directly. Firstly, a feedback controller is designed to make the system self-stabilizing. By designing suitable Lyapunov functional with adjustable parameters and combining with inequality techniques, two algebraic criteria are obtained to realize the GPS of the PDIMNNs. Owing to the conservatism caused by the ineluctable inequality scaling, it is worth noting that the controller gains are greater than the actual requirements. To further save control expenses, employing an adaptive controller to make the system stabilized. Finally, three numerical examples which sustain the usability of the obtained theoretical conclusions are shown. [ABSTRACT FROM AUTHOR]

In order to better describe the mechanism of rumor propagation, suppress rumor propagation. This paper examines the influence of true information disseminators on individuals and rumor spreaders who are affected by the public opinion environment. It is found by comparing those who have true information and those who do not have true information in the population. When there are true information disseminators in the crowd, it can effectively control the turn of individuals affected by the public opinion environment to the spread of rumors, and increase the turn of individuals affected by the public opinion environment to the true information disseminators. In addition, this paper analyzes the problems related to the optimal control of the rumor propagation model, and proposes some policies to increase the number of true information disseminators and reduce the number of rumor propagators, so as to better control the spread of rumors. Finally, numerical simulations verify the inhibitory effect of true information disseminators on the spread of rumors. It can be seen from the figure that the earlier the true information disseminator appears, the greater the number of true information disseminators, and the more obvious the inhibitory effect on the spread of rumors. • Add true information spreaders to the crowd, compared and discussed the differences with and without it. • Increasing the number of true information spreaders, can reduce the influence of public opinion and weaken rumor spreading. • It is concluded that the optimal control is helpful to reduce the number of rumors spreading in the crowd. [ABSTRACT FROM AUTHOR]

The property of reversibility is quite meaningful for the classic theoreticabl computer science model, cellular automata. This paper focuses on the reversibility of general one-dimensional (1D) linear cellular automata (LCA), under null boundary conditions over the finite field Z p . Although the existing approaches have split the reversibility challenge into two sub-problems: calculate the period of reversibility first, then verify the reversibility in a period, they are still exponential in the size of the CA's neighborhood. In this paper, we use two efficient algorithms with polynomial complexity to tackle these two challenges, making it possible to solve large-scale reversible LCA, which substantially enlarge its applicability. Finally, we provide an interesting perspective to inversely generate a 1D LCA from a given period of reversibility. [ABSTRACT FROM AUTHOR]

• P-Lingua is a well-known programming language for membrane systems. • P-Lingua 5 features customized P system variants definitions, including semantics. • Derivation modes and simulation directives are now described in P-Lingua 5 code. • P-Lingua 5 software toolkit (compiler and simulator) is backward-compatible. Membrane computing is a massively parallel and non-deterministic bioinspired computing paradigm whose models are called P systems. Validating and testing such models is a challenge which is being overcome by developing simulators. Regardless of their heterogeneity, such simulators require to read and interpret the models to be simulated. To this end, P-Lingua is a high-level P system definition language which has been widely used in the last decade. The P-Lingua ecosystem includes not only the language, but also libraries and software tools for parsing and simulating membrane computing models. Each version of P-Lingua supported new types or variants of P systems. This leads to a shortcoming: Only a predefined list of variants can be used, thus making it difficult for researchers to study custom ones. Moreover, derivation modes cannot be user-defined, i.e, the way in which P system computations should be generated is determined by the simulation algorithm in the source code. The main contribution of this paper is a completely new design of the P-Lingua language, called P-Lingua 5, in which the user can define custom variants and derivation modes, among other improvements such as including procedural programming and simulation directives. It is worth mentioning that it has backward-compatibility with previous versions of the language. A completely new set of command-line tools is provided for parsing and simulating P-Lingua 5 files. Finally, several examples are included in this paper covering the most common P system types. [ABSTRACT FROM AUTHOR]

A complex dynamical network (CDN) can be considered as the composition system with the nodes subsystem (NS) and the links subsystem (LS), and both subsystems are coupled with each other. Therefore, the dynamics of NS affects the dynamics of LS, and vice versa. In this paper, two vector differential equation (VDE)s are employed to describe the dynamics of NS and LS, respectively, in which the dynamics of NS is proposed as the VDE with the second derivative term (SDT) for more realistic engineering applications. Compared with the existing literature, the key advantage is that the dynamics of links is utilized to assist synthesizing the tracking controller of NS. The results show that when the state of LS tracks asymptotically the synthesized auxiliary tracking goal (ATG), the state of NS tracks asymptotically the given reference signal. Observing the eventual topology of CDN, all eventual links between nodes depend on the proposed ATG when NS achieving the tracking target, in which one special case is that if all target signals determined by ATG are not zero, all nodes are not isolated. Finally, the effectiveness of the proposed control scheme is verified by a numerical simulation example. [ABSTRACT FROM AUTHOR]

The topic of optimal antisynchronization control for unknown multiagent systems (MASs) with cooperative-competitive (i.e.,coopetition) interaction is challenging because of the complex connection characteristics and the coupling among agents. This paper proposes a reinforcement learning Algorithm based on coopetition strength (CS) to achieve optimal antisynchronization in unknown MASs. Firstly, an innovative CS function is explored, and the local state error information of the agents can be redefined. Furthermore, we propose a novel strategy iteration method to approximate the agent's optimal control policy. Meanwhile, the proposed algorithm's convergence analysis is presented, which is based on the Lyapunov stability and functional theorem. In the implementation of a data-based control policy, a network structure of actor-critic (AC) is designed. To improve the robustness of the control policy, the target network and experience replay (ER) are introduced in the training process. Finally, the algorithm's effectiveness is validated using comparable numerical simulations. [ABSTRACT FROM AUTHOR]

The topic of optimal antisynchronization control for unknown multiagent systems (MASs) with cooperative-competitive (i.e.,coopetition) interaction is challenging because of the complex connection characteristics and the coupling among agents. This paper proposes a reinforcement learning Algorithm based on coopetition strength (CS) to achieve optimal antisynchronization in unknown MASs. Firstly, an innovative CS function is explored, and the local state error information of the agents can be redefined. Furthermore, we propose a novel strategy iteration method to approximate the agent's optimal control policy. Meanwhile, the proposed algorithm's convergence analysis is presented, which is based on the Lyapunov stability and functional theorem. In the implementation of a data-based control policy, a network structure of actor-critic (AC) is designed. To improve the robustness of the control policy, the target network and experience replay (ER) are introduced in the training process. Finally, the algorithm's effectiveness is validated using comparable numerical simulations. [ABSTRACT FROM AUTHOR]

Competition is considered to be a driving force of many swarm systems in maintaining vitality, as important as collaboration. To model this competitive nature, an operation called k -winner-take-all (k WTA) is introduced that can output k maximum values from p input signals with 1 ⩽ k ⩽ p. In this paper, different projection functions (PFs) are combined with k WTA operations as activation functions for the first time, and a P- k WTA network is proposed, analyzed, and applied, which accelerates the convergence rate and enhances the robustness of existing models. With the aid of various characteristics, such as saturated or nonconvex, the convergent process of the proposed P- k WTA network is able to present diverse effects of PFs. Additionally, numerical simulations, an application to a multi-agent system, and an experiment of multiple E-puck robots on the CoppeliaSim platform all demonstrate the feasibility and effectiveness of the P- k WTA network. [ABSTRACT FROM AUTHOR]

This paper visits the design of event-triggered static and dynamic feedback controllers for discrete-time linear systems. For the efficiency of energy, we make the first attempt to devise a switching event-triggered mechanism (ETM) which is characterized by a switching between the discrete-time sampled-data control and the common continuous ETM. In this sense, the event detector has a waiting time interval after sending a sampling signal. This method facilitates to enlarge the lower bound of inter-execution intervals so that the number of samplings can be reduced essentially. The issue of actuator saturation is also considered in this paper. By generalized sector condition, a switching Lyapunov functional is constructed to derive the sufficient conditions such that the discrete-time linear systems are local stable. Based on the stability conditions, the calculation methods are provided to solve both the desired control gains and the triggering parameters. Finally, numerical simulations are given to validate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Understanding the behavior of an infection network is typically addressed from either a microscopic or a macroscopic point-of-view. The trade-off is between following the individual states at some added complexity cost or looking at the ratio of infected nodes. In this paper, we focus on developing an alternative approach based on dynamical linear systems that combines the fine information of the microscopic view without the associated added complexity. Attention is shifted towards the problems of source localization and network topology discovery in the context of infection networks where a subset of the nodes is elected as observers. Finally, the possibility to control such networks is also investigated. Simulations illustrate the conclusions of the paper with particular interest on the relationship of the aforementioned problems with the topology of the network and the selected observer/controller nodes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the sampled-position states based consensus of networked multi-agent systems with second-order dynamics, where agents are connected through communication channels subject to time-varying communication delays. Note that consensus in such systems cannot be reached if using only the current sampled-position states. This paper investigates the positive effects of time-varying communication delays on the consensus. For two cases, where a directed graph has a fixed or switching topology, several sufficient conditions on consensus are derived by using a discretized Lyapunov-Krasovskii functional method. Based on the obtained conditions, suitable control protocols can be devised through tuning two parameters. Finally, simulation shows that consensus can be achieved using sampled-position states if the time-varying communication delays are within a certain time interval with its lower bound strictly greater than zero. [ABSTRACT FROM AUTHOR]

• The leaderless consensus of a class of strict-feedback nonlinear systems with uncertainties under actuator and sensor attacks is considered. • All the closed-loop signals are globally uniformly bounded. • Asymptotical consensus can be guaranteed with distributed adaptive leaderless consensus strategy under cyber-attacks. In this paper, the leaderless consensus control of a group of high-order strict feedback nonlinear systems with uncertainties under sensor and actuator attacks is considered. A fully distributed adaptive leaderless consensus control strategy is proposed to deal with the sensor and actuator attacks. Compared with existing results in which the model considered is simple, a strict-feedback nonlinear system is considered in this paper, which makes the problem more challenging. It is shown that all closed-loop signals are uniformly bounded and asymptotic output consensus is achieved in presence of sensor and actuator attacks. Simulations are given to demonstrate the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

Artificial bee colony programming (ABCP) is a novel evolutionary computation based automatic programming method, which uses the basic structure of artificial bee colony (ABC) algorithm. In this paper, some studies were conducted to improve the performance of ABCP and three new versions of ABCP are introduced. One of these improvements is related to the convergence performance of ABCP. In order to increase the local search ability and achieve higher quality solutions in early cycles, quick ABCP algorithm was developed. Experimental studies validate the enhancement of the convergence performance when the quick ABC approach is used in ABCP. The second improvement introduced in this paper is about providing high locality. Using semantic similarity based operators in the information sharing mechanism of ABCP, semantic ABCP was developed and experiment results show that semantic based information sharing improves solution quality. Finally, combining these two methods, quick semantic ABCP is introduced. Performance of these novel methods was compared with some well known automatic programming algorithms on literature test problems. Additionally, ABCP based methods were used to find approximations of the Colebrook equation for flow friction. Simulation results show that, the proposed methods can be used to solve symbolic regression problems effectively. [ABSTRACT FROM AUTHOR]

Quantum vision representation (QVR) is the foundation of quantum vision information processing, which is a possible solution to store and process massive visual data efficiently. In this paper, firstly, quantum image representations are divided into three categories based on different methods of color information storage. Secondly, in order to systematize quantum image representation, we propose five new methods. Thirdly, we develop models of QVR by extending three categories of quantum image representations into corresponding QVRs. Next, we design and implement 1D, 2D, and 3D quantum transforms based on QVR for the first time. Simulation experiments demonstrate that proposed multi-dimensional quantum transforms are effective. In conclusion, this paper develops a model of QVR and provides a feasible scheme for multi-dimensional quantum transforms to be applied in quantum vision information processing. [ABSTRACT FROM AUTHOR]

• This paper is to find the truth Z of events in social sensing with information flows. • A truth discovery model is presented which considers the individuals' reliability R. • The model also considers the dependency D between individuals' observations. • An iterative expectation maximization is presented to infer both Z, R and D. • Extensive experiments demonstrate the effectiveness of the presented algorithm. Social sensing relies on a large number of observations reported by different, possibly unreliable, agents to determine if an event has occurred or not. In this paper, we consider the truth discovery problem in social sensing, in which an agent may receive another agent's observation (known as an information flow), and may change its observation to match the observation it receives. If an agent's observation is influenced by another agent, we say that the former is a dependent agent. We propose an Iterative Expectation Maximization algorithm for Truth Discovery (IEMTD) in social sensing with dependent agents. Compared with other popular truth discovery approaches, which assume either the agents' observations are independent, or their dependency is known a priori, IEMTD allows to infer each agent's reliability, the observations' dependency and the events' truth jointly. Simulation results on synthetic data and three real world data sets demonstrate that in almost all our experiments, IEMTD achieves a higher truth discovery accuracy than the existing algorithms when dependencies exist between agents' observations. [ABSTRACT FROM AUTHOR]

This paper studies the discrete-time multiagent consensus problem with kinds of diverse interaction distortions which are related to the states of agents, and a novel dual switching control strategy is proposed, named echo control, to make the system achieve state consensus. The main feature of the echo control strategy is that agents resend the information flows (received from neighbors) to the corresponding neighbor, just like the reflecting of sound waves. In other words, every individual under the echo control strategy is not only the sender and receiver of the information flows, but also the reflector of its neighbors' messages. In this way, a negative feedback loop is constructed between every two agents, which is designed to ensure the stability of the multiagent system (MAS), and then the agents' states will converge to a common value. The effectiveness of the echo control protocol is theoretically analyzed, and numerical simulations validate the correctness of the relevant results. [ABSTRACT FROM AUTHOR]

This paper deals with the fixed-time stabilization (FXTS) and preassigned-time stabilization (PATS) problems for delayed memristive neural networks (DMNNs) via a unified controller. Firstly, we design a delayed controller to realize the FXTS of DMNNs. Moreover, the sufficient conditions and accurate time estimation of FXTS are presented. It is proved that the system states can achieve stabilization within a fixed time without being affected by the initial states. Then, by modifying the parameters of the same controller, DMMNs achieve stabilization within an arbitrary preassigned time, which gets rid of the limitation of system and controller parameters. Finally, the feasibility and validity of the main results are verified by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper deals with the fixed-time stabilization (FXTS) and preassigned-time stabilization (PATS) problems for delayed memristive neural networks (DMNNs) via a unified controller. Firstly, we design a delayed controller to realize the FXTS of DMNNs. Moreover, the sufficient conditions and accurate time estimation of FXTS are presented. It is proved that the system states can achieve stabilization within a fixed time without being affected by the initial states. Then, by modifying the parameters of the same controller, DMMNs achieve stabilization within an arbitrary preassigned time, which gets rid of the limitation of system and controller parameters. Finally, the feasibility and validity of the main results are verified by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper investigates continuous-time complex dynamical networks (CDNs) with dynamic connection relationships. The CDNs are regarded as interconnected systems composed of intercoupled subsystems of links and nodes. Different from previous research, a simplified and intuitive vector dynamic model is employed to describe the links subsystem (LS) rather than a matrix equation. Because the state of the links cannot be measured accurately in practice, the stabilization and synchronization of the nodes subsystem (NS) are investigated via the auxiliary role of the LS. Using the proposed node controllers and coupling forms in the LS, the CDNs can be stabilized and synchronized. Finally, numerical simulations are presented to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This study addresses the problem of observer design and its applications to attack detection for an interconnected cyber-physical system subjected to unknown disturbances and sensor attacks. First, an overall system is constructed using the information on the interconnection of several subsystems, and problems related to the observer design are then solved. Second, to facilitate the residual's sensitivity to attacks and its robustness against unknown disturbances, an observer is provided. The gain matrix is developed using both the H − performance and mixed L 2 − L ∞ / H ∞ performance. Third, for the suppression of the effect of unknown disturbances and attack signals on the errors in the state estimation and on attack detection in a finite-frequency domain, the generalized Kalman-Yakubovich-Popov lemma is applied to set the conditions under which the observer can operate effectively. In light of the detection scheme, we suggest a detection logic that prevents false alarms by comparing the evaluation function to the corresponding threshold. Additionally, an interval threshold-based method is introduced to establish the threshold bounds, and numerical simulation results are provided to demonstrate the method's validity in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the exponential synchronization of multilayer neural networks (MNNs) is investigated via alternate periodic event-triggered control (APETC). Distinguished from the previous work, a novel APETC which incorporates aperiodically intermittent control (AIC) and periodic event-triggered mechanism is firstly proposed. Determined by two event-triggered conditions, the control and rest intervals of APETC are based on the present state of the system rather than being predetermined. In contrast to the conventional event-triggered control (ETC), the event-triggered conditions of APETC can not only judge the updates of control signals, but also dominate the actuation and close of the controller. Moreover, by introducing the sampling period into ETC, the number of event triggers can be decreased and the Zeno phenomenon is completely avoided. Subsequently, synchronization criteria for MNNs under APETC are established on the basis of Lyapunov method and graph theory. Finally, several numerical simulations are performed to demonstrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

• We discuss the use of incomplete and self-confident preferences in multicriteria decision making. • We reveal the condition under which incomplete preference relations are better. • We reveal the condition under which self-confident preference can improve decision quality. • A sensitivity analysis is presented to make the experimental conclusions more robust. Incomplete preference relations and self-confident preference relations have been widely used in multicriteria decision-making problems. However, there is no strong evidence to validate their use in decision-making in the current literature. This paper reports on the design of two bounded rationality principle based simulation methods, and detailed experimental results that aim at providing evidence to answer the following two questions: (1) what are the conditions under which incomplete preference relations are better than complete preference relations?; and (2) can self-confident preference relations improve the quality of decisions? The experimental results show that when the decision-maker is of medium rational degree, incomplete preference relations with a degree of incompleteness between 20% and 40% outperform complete preference relations; otherwise, the opposite happens. Furthermore, in most cases the quality of the decision making improves when using self-confident preference relations instead of incomplete preference relations. The paper ends with the presentation of a sensitivity analysis that contributes to the robustness of the experimental conclusions. [ABSTRACT FROM AUTHOR]

Highlights • A distributed framework for communication and collaboration among connected objects is presented in this paper which can be applied for many critical issues. • We apply computational negotiation models for connected objects which are expected to be an important issue for edge intelligence. • We take two emergent IoT use cases for smart cities to evaluate the performances of our approach. Abstract In this paper, we propose a computational negotiation approach on Internet of Things (IoT) system where distributed edge devices can make their own decisions for smart applications. Particularly, Artificial Intelligence (AI) techniques play an important role of edge analytics in order to adaptively improve the performance of IoT systems. In this regard, we apply several AI techniques to provide negotiation models (e.g., synchronization, competition, and cooperation) among connected objects for edge analytics. Moreover, in the context of smart city, two typical use cases on IoT applications have been presented to evaluate our proposed approach. They are i) smart traffic control and ii) smart home energy management system. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that the initial spreaders play an important role in the spread of information in complex networks. Thus, a variety of centrality-based methods have been proposed for identifying the most influential spreaders. However, most existing studies overlook the fact that, in real social networks, it is more costly and difficult to convince influential individuals to act as initial spreaders, resulting in a high risk to maximal spreading. In this paper, we address this problem on the basis of the assumption that the activation of large-degree nodes carries a higher risk than that of small-degree nodes. We aim to identify the initial spreaders that most effectively maximize the spreading when considering both the activation risk and the outbreak size of the initial spreaders. Analysis of random networks reveals that the degree of the optimal initial spreaders does not correspond to the largest node degree in the network, but is instead determined by the infection probability and difference in activation risk among nodes with different degrees. We propose a risk-aware metric to identify the most effective spreaders in real networks. Numerical simulations show that this risk-aware metric outperforms the existing benchmark centralities in terms of maximizing the spreading. [ABSTRACT FROM AUTHOR]

In this paper, we investigate bipartite containment control problem of multi-agent systems (MASs) with signed directed graph under adversarial inputs. Firstly, we define the bipartite containment error and establish the equivalence between the bipartite containment error converging to zero and the achievement of bipartite containment control. Subsequently, we prove that the bounded L 2 -gain bipartite containment problem under adversarial inputs can be reformulated as a multi-player zero-sum differential graphical game problem and can be solved via the solution to the coupled Hamilton-Jacobi-Isaacs (HJI) equation. To address this, we propose a policy iteration (PI) algorithm and prove its convergence under different updating cases. The proposed algorithm is implemented by neural networks (NNs) and a numerical simulation example is provided to show its effectiveness. [ABSTRACT FROM AUTHOR]

This paper discusses transmission power scheduling and control co-design in wireless sensor networks (WSNs) under communication energy constraints, in which the dynamics of all mobile sensor nodes (MSNs) are assumed to be nonlinear. A most regular code sequence (MRCS) is introduced as the transmission power scheduling policy to automatically allocate the transmission power levels to MSNs thereby adhering to the given communication energy budget. The effects of the different transmission power levels on data transmission is explicitly characterized in this paper. By using a stochastic system analysis approach and average dwell time technique, a framework for the transmission power scheduling and control co-design is established. The control co-design algorithm is proposed to simultaneously determine the gain of the sampled-data feedback controller and the parameters of the generated MRCS. A numerical simulation is conducted to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multimodal user identification and verification scheme combining two inter-linked biometric traits, i.e., signature and brain signals (Electroencephalography (EEG)). Moulding handwritten strokes for signature stimulates EEG signals in the brain. The response of the brain signals during signing is unique for each user and this brain signal-signature relation is utilized in the proposed scheme. To the best of our knowledge, there exist no authentication approach combining these two inter-linked biometric traits. The proposed multimodal scheme uses Hidden Markov Model (HMM) based sequential classifier to model features extracted from signatures and EEG signals individually. Pyramid Histogram of Orientation Gradients (PHOG) features are extracted from the signature-images and next PHOG features are used to build user specific signature-HMM models. Similarly, user wise EEG-HMM models are built with Daubechies-4 (DB4) wavelet analysis. Finally, a score combining classification scores of signature-HMM and EEG-HMM models is used to perform user identification and verification. To evaluate the effectiveness of the proposed scheme, we have developed a dataset collecting these two traits simultaneously using the Emotiv Epoc+ device and pen-paper for 70 individual subjects. Thereafter, user’s identification is performed with individuals’ signature and EEG signals as well as their combined traits. The identification accuracy of the proposed multimodal approach has been achieved upto 98.24%. The effectiveness of the verification scheme is validated by the Receiver Operating Characteristic (ROC) curves. [ABSTRACT FROM AUTHOR]

In this paper, from the perspective of opinion dynamics theory, we investigate the interaction mechanism of a group of autonomous agents in an e-commerce community (or social network), and the influence power of opinion leaders during the formation of group opinion. According to the opinion's update manner and influence, this paper divides social agents within a social network into two subgroups: opinion leaders and opinion followers. Then, we establish a new bounded confidence-based dynamic model for opinion leaders and followers to simulate the opinion evolution of the group of agents. Through numerical simulations, we further investigate the evolution mechanism of group opinion, and the relationship between the influence power of opinion leaders and three factors: the proportion of the opinion leader subgroups, the confidence levels of opinion followers, and the degrees of trust toward opinion leaders. The simulation results show that, in order to maximize the influence power in e-commerce, enhancing opinion leaders’ credibility is crucial. [ABSTRACT FROM AUTHOR]

• The PIR protocol is improved using multiple intentional delays as tunable parameters for second-order multi-agent systems. • A systematic and flexible parameter tuning strategy is presented for our proposed PIR protocol. • The superiority of our PIR protocol is demonstrated by developing a comprehensive comparison. • Our PIR protocol is superior in noise attenuation over the PID scheme. • Our PIR protocol outperforms the PR protocol regarding consensus speed, parameter robustness, and disturbance resistance. The attractive characteristics of an innovative delay-based controller called Proportional-Integral-Retarded (PIR) controller are reported in many fields. However, a detailed investigation of the merits of the PIR controller for multi-agent systems (MASs) is still missing. To bridge this gap, we propose an improved PIR protocol for a general class of second-order MASs. This protocol, using multiple intentional delays and heterogeneous gains as tunable parameters, realizes the optimal spectrum assignment of each subsystem in a completely independent way without affecting the stability of other subsystems. We then present a systematic and flexible parameter tuning strategy for the PIR protocol such that the desired exponential decay rate of the consensus system can be achieved. Specifically, this strategy achieves an arbitrary dominant root assignment in the domain of non-negative real numbers and enables enhanced control flexibility by adjusting an additionally introduced free variable in the analytical parameter formulae. To unveil the superiority of our proposed PIR protocol, we develop a comprehensive comparison of the PIR protocol with the Proportional-Integral-Derivative (PID) scheme and the Proportional-Retarded (PR) protocol. The numerical simulations show that our PIR protocol outperforms the PID scheme regarding the noise attenuation property. Besides, this protocol can attain faster consensus, stronger robustness against parameter uncertainties, and superior disturbance resistance than the PR protocol. Our paper provides a valuable reference for the design of an effective and implementable multi-agent consensus protocol. [ABSTRACT FROM AUTHOR]

In the face of an emergency, regionally coordinated allocation is an important prerequisite for maintaining the normal order of production and living. Considering the complexity and uncertainty of the emergencies in local governments, this paper establishes a two-stage process for allocating medical resources. In the first stage, cooperative regions with incomplete weights and multidimensional uncertain information ranked via a novel decision-making method. The second stage is an optimal strategy which the optimization model is established to minimize the cost incurred by the local government considering two components of cost. The first stage is the reserve cost for the local government to prepare a certain amount of flexible procurement materials to respond to a public health emergency. The second is the scheduling cost when cooperative regions support medical resources. Theoretically, the optimal allocation of the reserve of different kinds of medical resources is deduced at the lowest cost. Through numerical simulation and sensitivity analysis, we explore the impacts of attribute weights, resource reserve capacity, unit costs, and the number of cooperative regions on the demand for different kinds of medical resources and the total costs incurred by local governments. This study provides a regional coordinated allocation framework and theoretical reference for government departments responding to the emergencies. [ABSTRACT FROM AUTHOR]

This paper studies passivity analysis and stability analysis for neural networks with time-variant delay. The time-variant delay is presumed to respond in trigonometric form. Firstly, a novel interval approximation method is constructed with adjustable parameters, which can be adjusted to reduce conservatism. According to the feature of time delay in trigonometric form, the specific allowable delay set can be obtained. Secondly, by introducing the delay-product-type parts in Lyapunov-Krasovskii functional (LKF), the emphatically improved passivity criterion and stability criterion with less conservatism are obtained by using the advanced inequalities. Finally, two numerical simulations are listed where our work can obtain larger maximum allowable delay bounds compared with other works, which demonstrate that the results based on the interval approximation method are less conservative. [ABSTRACT FROM AUTHOR]

This paper pays attention to investigating the exponential stability of stochastic multi-layer complex network with regime-switching diffusion (SMLCNRSD). Different from the existing stability results on stochastic complex network, what we research is to consider the regularity of the solution process under the local conditions, and make use of the truncation method to obtain the global existence and uniqueness of the solution. Using graph theory, Lyapunov method, as well as some stochastic analysis techniques, several sufficient criteria for exponential stability are attained by aperiodically intermittent control. Finally, the theoretical results are applied to study the exponential stability of a two-layer Hindmarsh-Rose neuron network with regime-switching diffusion. Moreover, the effectiveness of the results provided has been well demonstrated through numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, the memory property and the historical dynamic information of a fractional system via event-triggered control are studied. A unit step function is introduced and the control input is taken as a piecewise function. By theorem analysis and numerical simulations, it is shown that the fractional calculus of a piecewise function is relevant to the entire historical dynamic information and cannot be calculated in segments because of its memory property. So, the state of a fractional system via event-triggered control is affected by all input from the initial time, but not the triggering time, to the current time. But in previous studies, this property is neglected and the historical dynamic information has not been fully studied, which disagrees with the real physical systems. Therefore, the historical dynamic information of a fractional system via event-triggered control should be studied in order to satisfy the demands in engineering. [ABSTRACT FROM AUTHOR]

In order to cope with uncertainties in a platoon, this paper proposes a reconfigurable multi-agent architecture to address the platoon safety problem by handling two modes: the normal mode and the degraded mode. At this stage of research, the normal mode is characterized by the interaction between agents over a Vehicle-to-Vehicle (V2V) communication network while the degraded mode simply involves sensors for a local perception. The switching from the normal mode to the degraded one is triggered when the communication quality is considered not fully reliable. A PID (Proportional Integral Derivative) controller is proposed to regulate the inter-vehicle distance and orientation. Two models are proposed in this paper: in the first one, the management operations such as splitting and joining are set up while the second is mainly modeled to assess the implemented controller quality. In this paper, the safety of a platoon is represented by the quality of tracking and the inter-vehicle distance. The mentioned features are assessed for both modes through a formal verification using the Uppaal software. We prove the efficiency of the proposed platoon model for several situations such as merging, following or leaving the platoon by verifying different properties using the model checking. The evaluation of the second model, simulated by the Webots software, proves the impact of the number of vehicles on the platoon performance and the vehicle tracking quality. We conclude that when the platoon reaches a certain number of vehicles, the safety criterion is no more reliable. [ABSTRACT FROM AUTHOR]

This paper devotes to solving time-varying linear matrix equations (TVLMEs) from the viewpoint of high-order neural networks. For this purpose, high-order zeroing neural network (ZNN) models are designed and studied to solve TVLMEs. Compared with the first-order ZNN model for TVLMEs, the proposed high-order ZNN models are based on the design of the high-order error functions, and different order choices will generate different high-order ZNN models. Two nonlinear activation functions [i.e., tunable activation function (TunAF) and sign-bi-power activation function (SBPAF)] are used to speedup the high-order ZNN models for achieving the finite-time convergence. Furthermore, the strict theoretical analyses are provided to show that high-order ZNN models have better properties (especially in terms of convergence), when the nonlinear activation functions are used. Two numerical simulations are given to reveal the superior convergence property of the proposed high-order ZNN models, as compared to the first-order ZNN model for solving TVLMEs. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stochastic stability and stabilization problem for a class of continuous-time semi-Markovian jump systems (S-MJSs) with mode transition-dependent sojourn-time distributions. In contrast to other approaches, this paper offers the possibility to explore more refined probability models on the sojourn-time by considering the mode transition-dependent sojourn-time distributions instead of mode-dependent ones. Here, to construct a finite set of linear matrix inequalities (LMIs) from sojourn-time-dependent matrix inequalities, a less conservative relaxation method is proposed in such a way that all available constraints on time-varying transition rates can be exploited. Simulation examples report the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes two complex-valued zeroing neural network (Cv-ZNN) models for solving dynamic complex time-variant linear equations. The models involve two complex-valued nonlinear processing methods and adapt two real-valued activation functions. The convergence and robustness of the two Cv-ZNN models are analyzed comprehensively. Firstly, the convergence discussion shows that the corresponding stable error can rapidly converge to zero in finite time, with the upper limit of constriction time calculated. The upper limit of stable error is successfully obtained when the model implementation error is injected into the Cv-ZNN models, which displays their superior robustness. Besides, the results of numerical simulations reveal that the Cv-ZNN models effectively address the time-variant linear system of equations over complex field, even in noisy environments. The simulation results also demonstrate that two novel nonlinear activation functions perform much better than the Linear, Power, Bipolar Sigmoid Power-Sigmoid and Sign-bi-power activation functions. [ABSTRACT FROM AUTHOR]

The secure message recovery problem in presence of cyber-attack is considered in this paper. The message is modulated by a master chaotic system which may be vulnerable to a cyber-attack and then transmitted to a receiver. By designing a novel observer at the receiver side, the message is recovered correctly. All the design process is performed in discrete-time domain which is suitable for digital communications. Besides, there are no restrictions on the message signal and nonlinear term signature matrix which makes the proposed approach more suitable in the real applications. Numerical simulations are introduced to show the feasibility and the superior performance of the proposed scheme in comparison with the conventional methods. [ABSTRACT FROM AUTHOR]

Effectively measuring the information content of Basic Probability Assignments (BPA) is a crucial prerequisite for addressing uncertain information within the framework of Dempster-Shafer theory. In previous research, uncertainty and dissimilarity measures for BPAs were often treated separately to counteract the impact of incomplete mutual exclusion between focal elements. In this paper, we introduce a Transient Mass Function (TMF) based on the process of pignistic probability transformation to forge a link between probabilistic and evidential information. As a transient state of BPA, the TMF not only considers the structural features of the focal element but also maintains its numerical content. Carrying out probabilistic information measures on the TMF is equivalent to performing belief information measures on the original BPA. Finally, validated by numerical simulations, TMF provides a unified and effective metric framework for evidential information. [ABSTRACT FROM AUTHOR]

This paper presents a new measurement-based control design method for cancer chemotherapy. Cancer chemotherapy aims basically at simultaneously eradicating or significantly reducing the number of cancer cells and maintaining tolerable levels of drug concentration and toxicity. To achieve such aim, drugs are often injected into the patient’s body according to a drug schedule specifying the drug dose and delivery time. Several strategies for planning cancer chemotherapy have been developed in the literature, where evolutionary algorithms have been applied to find optimal drug schedules of cancer treatment under constraints on some key treatment parameters such as drug concentration and toxic side effects. In such methods, the amount of drug doses, delivered in the body at each time during the treatment, does not depend on the current drug concentration, toxicity level, and/or number of cancer cells. Successful design of chemotherapy drug scheduling requires the availability of an accurate mathematical model that perfectly predicts the number of cancerous cells and describes effects of treatment. Several models with either complex or simple structures are available in the literature. Complex-structure models are proposed to deeply understand interactions between cancer and normal cells that affect the performance of the cancer chemotherapy. Nevertheless, such complex models are based on a high-order set of differential equations which can be difficult to solve. Simple-structure models, which are often obtained on the basis of some simplifying assumptions, can be viewed only as an approximation of the cancer system. Hence, designing chemotherapy drug schedules on the basis of simplified models may result in unsuccessful cancer treatment. Unlike conventional control strategies for cancer chemotherapy, our attempt in this paper is to address the problem of designing a control system for cancer treatment using a set of frequency-domain data. Hence, a two-degree-of-freedom PID (proportional-integral-derivative) control scheme is proposed to control cancer growth. These PID controllers are designed to simultaneously provide the optimal amount of drug doses to be delivered into the patient’s body according to the current drug concentration and toxicity level, and maintain the drug concentration and toxicity levels within their pre-specified ranges. The proposed cancer control technique is validated through a first simulation example. Another example to control biological systems is also presented to show the feasibility of the proposed method. Simulation results obtained have demonstrated the capability of the proposed control scheme to address cancer chemotherapy problems. [ABSTRACT FROM AUTHOR]

• A novel dictionary learning method was proposed for process monitoring. • The geometry information was fused into the penalty term of sparse coding. • The discriminative sparse coding was conducive to mode identification. • The effectiveness of the method was illustrated by extensive experiments. • The proposed method was also effective for fault isolation in industrial system. Data-driven process monitoring methods have attracted great attention due to the case that it can provide an efficient way to cope with the industrial process without the need of first-principle models. The local information that results from the reaction process often influences the process monitoring result. Unfortunately, this local information is usually ignored in the data-driven process monitoring method. In this paper, a geometry constrained dictionary learning (GCDL) method is proposed to address the above problem. By exploiting the underlying characteristics and simultaneously holding the local geometrical information, the GCDL method leads to a balance between the reconstructive item and the discriminative item. Inspired by the manifold method, the GCDL method can provide discriminative sparse coding, which means that samples that belong to the same class will share similar sparse coding. It is in accordance with the fact that the samples of different categories often reside on different subspaces embedded in a high-dimensional feature space. The effectiveness of the proposed method is evaluated through two numerical simulation cases, a continuous stirred tank reactor (CSTR) case and a real industrial aluminum electrolysis process. Extensive experimental results reveal that the proposed method shows better performance by taking the local geometrical information into consideration. [ABSTRACT FROM AUTHOR]

• Local and global information of multi-unit industrial process are considered. • Nonlinear relations between multivariate can be solved by deep learning technique. • Joint feature is defined to construct the global model. • The proposed method can provide better fault information. • Numerical example and TE benchmark are adopted to test the proposed method. Determining whether a fault occurs locally or globally is highly important for large-scale industrial processes involving multiple operating units. Moreover, the complex non-linearity among process variables is a prominent feature of modern industries. This paper proposes a distributed-ensemble stacked autoencoder (DE-SAE) model based on deep learning technology for monitoring non-linear, large-scale, multi-unit processes. First, the deep features of the variables involved in each operating unit are extracted with the stacked autoencoder (SAE) to represent the essential structure of the unit. Two statistics are separately constructed using the deep features and the reconstruction error for detecting the faults in local units. Subsequently, the deep representations of the variables from each operating unit are modeled with the SAE to extract the global information for global monitoring. The proposed DE-SAE model uses deep learning techniques to solve the complex non-linear relationships in industrial processes, while considering their local and global information. Therefore, the method can explain the monitoring results better. Experimental results obtained from the numerical simulation and Tennessee-Eastman process confirm the feasibility and superiority of this method. [ABSTRACT FROM AUTHOR]

This paper addresses the leader-tracking and the containment control problems for heterogeneous high-order Multi-Agent Systems (MASs) sharing information through a directed communication topology. To solve both the control problems, a fully-distributed Proportional-Integral-Derivative (PID) control strategy is proposed, whose stability is analytically proven by exploiting the regulator equations and the Static Output Feedback (SOF) procedure adapted to the MASs framework. The application of SOF allows recasting the PID control design problem into a state-feedback control design one and, hence, finding the proper values of the proportional, integral and derivative actions via classical state-feedback approaches, such as the Linear-Quadratic-Regulator (LQR) strategy. Numerical simulations confirm the effectiveness of the proposed approach in guaranteeing that each follower tracks the leader behavior in the case of leader-tracking and that each follower converges to the convex hull spanned by the multiple leaders in the case of containment control. [ABSTRACT FROM AUTHOR]

In the paper “A Semiring-based study of judgment matrices: properties and models” [Information Sciences 181 (2011) 2166–2176], the semiring is applied to the discussion of judgment matrix properties. This paper shows that the suggested semirings could be replaced by a more convenient algebraic structure. [ABSTRACT FROM AUTHOR]

Fuzzy logic control is a recognized approach for handling the faced uncertainties within control applications. However, type-1 fuzzy controllers using crisp type-1 fuzzy sets might not be able to fully handle the high levels of uncertainties and nonlinear dynamics associated with real world control applications. On the other hand, interval type-2 fuzzy controllers using Interval Type-2 Fuzzy Sets (IT2-FSs) might be able to handle such uncertainties to produce a better control performance. However, the systematic design of interval type-2 fuzzy controllers is still a challenging problem due to the difficulty in determining the parameters of the IT2-FSs. In this paper, we will present the novel application of Big Bang-Big Crunch optimization (BB-BC) approach to optimize the antecedent membership parameters of Interval Type-2 Fuzzy PID (IT2-FPID) controllers in a cascade control structure. Since the IT2-FPID control structure involves more design parameters compared to its type-1 counterpart, it is beneficial to employ the BB-BC method which has a low computational cost and a high convergence speed. The presented BB-BC based optimized IT2-FPID cascade structure will be compared with its Type-1 Fuzzy PID (T1-FPID) and conventional PID controller counterparts which were also optimized with the BB-BC optimization. In addition, the proposed IT2-FPID structure will be compared with a self-tuning T1-FPID control structure. We will then present the novel application of the cascade control architecture to solve the path tracking control problem of mobile robots which inherits large amounts of uncertainties caused by the internal dynamics and/or feedback sensors of the controlled system. Several experiments were performed in simulation and in real world using the PIONEER 3-DX mobile robot which will act as a platform to evaluate the proposed control systems in this paper. The results illustrated that the IT2-FPID structure enhanced significantly the control performance even in the presence of uncertainties and disturbances when compared to the PID, T1-FPID and self-tuning T1-FPID structures. Moreover, it has been shown that the reason for the superior control performance of the IT2-FPID under high levels of uncertainty and noise is not merely for its use of extra parameters, but rather its different way of dealing with the uncertainties and noise present in real world environments by comparing with a self-tuning T1-FPID structure. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a vectorized matrix parameters encoding aspect for an evolutionary computer vision approach to procedural tree modeling. A serialized fixed-size floating-point encoded tree parameter set consists of a set of auxiliary local and other global parameters. The main goal of paper is to lower problem dimensionality needed for encoding local parameters. For evolution simulation, differential evolution algorithm is used. The optimizer evolves a parameterized procedural model by fitting a set of its rendered images to a set of automatically preprocessed reference photo images. The reconstructed tree morphology is then used for reconstructed tree animation, to generate similar geometrical tree models based on similar morphology. Examples of reconstructed model animation are shown, such as simulation of its growth, sway in the wind, or adding leaves. [Copyright &y& Elsevier]

Mobile crowdsensing (MCS) has shown great potential in addressing large-scale data sensing problem by allocating sensing tasks to pervasive mobile users (MU). The MUs will participate in the MCS if they can receive sufficient compensation. Existing work has designed lots of incentive mechanisms for MCS, but ignores the MUs' resource demand uncertainties that is critical for resource-constrained mobile devices. In this paper, we propose to design an incentive mechanism for MCS by taking the MUs' own resource demand into the economic model. As different MUs will have different behavior, they will participate in the MCS with different levels. Based on this idea, we formulate the incentive mechanism by using the Stackelberg game theory. Furthermore, a dynamic incentive mechanism (DIM) based on deep reinforcement learning (DRL) approach is investigated without knowing the private information of the MUs. It enables the SP to learn the optimal pricing strategy directly from game experience. Finally, numerical simulations are implemented to evaluate the performance and theoretical properties of the proposed mechanism and approach. [ABSTRACT FROM AUTHOR]

This paper proposes two methods to investigate the problem of finite-time Mittag-Leffler synchronization for the systems of fractional-order quaternion-valued neural networks (FQVNNs) with two kinds of activation functions, respectively. Generally, the first method mainly reflects in the new establishment of Lyapunov-Krasovskii functionals (LKFs) and the novel application of a new fractional-order derivative inequality which contains and exploits the wider coefficients with more values. Meanwhile, the second one is embodied in the comprehensive development of both the norm comparison rules and the generalized Gronwall-Bellman inequality with the help of Laplace transform of Mittag-Leffler function. Thanks to the above two methods, the flexible synchronization criteria are easily and separately obtained for the studied four systems of FQVNNs with general activation functions and linear threshold ones. Finally, two numerical simulations are given to demonstrate the feasibility and effectiveness of the newly proposed approaches. [ABSTRACT FROM AUTHOR]

• A fast hypervolume approximation algorithm is proposed that accelerates the hypervolume calculation with a steady accuracy error, through a segmentation strategy and the Monte Carlo simulation. The proposed algorithm achieves ten times faster than other compared algorithms in experiments. • Base on above the fast approximation method, an incremental version algorithm is proposed to solve many objective optimization problem and promotes the performance of hypervolume based algorithms. Hypervolume indicator based evolutionary algorithms have been reported to be very promising in many-objective optimization, but the high computational complexity of hypervolume calculation in high dimensions restrains its further applications and developments. In this paper, we develop a fast hypervolume approximation method with both improved speed and accuracy than the previous approximation methods via a new segmentation strategy. The proposed approach consists of two crucial process: segmentation and approximation. The segmentation process recursively finds areas easy to be measured and quantified from the original geometric figure as many as possible, and then divides the measurement of the rest areas into several subproblems. In the approximation process, an improved Monte Carlo simulation is developed to estimate these subproblems. Those two processes are mutually complementary to simultaneously improve the accuracy and the speed of hypervolume approximation. To validate its effectiveness, experimental studies on four widely-used instances are conducted and the simulation results show that the proposed method is ten times faster than other comparison algorithms with a same measurement error. Furthermore, we integrate an incremental version of this method into the framework of SMS-EMOA, and the performance of the integrated algorithm is also very competitive among the experimental algorithms. [ABSTRACT FROM AUTHOR]

Fuzzy linguistic preference relations (FLPRs) are an efficient way to express qualitative judgments of decision makers (DMs). This paper proposes a new group decision making (GDM) method based on the multiplicative consistency and consensus of FLPRs. First, we propose the concepts of consistency index and acceptable multiplicative consistent FLPRs. Then, we propose a method to improve the consistency of a FLPR, by which an acceptable multiplicative consistent FLPR is derived. Subsequently, we propose the consensus index for measuring the agreement among DMs. With respect to some FLPRs have an unacceptable multiplicative consistency and an unacceptable consensus, we propose an integer programming model to make both their consistency and consensus better to yield improved FLPRs with acceptable multiplicative consistency and consensus. Furthermore, the DMs' comprehensive weight vector is determined. Then, we propose a new GDM algorithm following the multiplicative consistency and consensus of FLPRs. Finally, the feasibility and the applicability of the proposed GDM method are illustrated via an application example and some comparative analyses with the existing GDM methods. [ABSTRACT FROM AUTHOR]