Output regulation theory is an effective method for achieving accurate time-varying command following and can utilize adaptive internal models to follow arbitrary reference signals generated by an exosystem. However, ...
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This paper is devoted to event-triggered synchronization of delayed memristive neural networks with H∞and passivity *** aim is to guarantee the exponential synchronization and mixed H∞and passivity control for memri...
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This paper is devoted to event-triggered synchronization of delayed memristive neural networks with H∞and passivity *** aim is to guarantee the exponential synchronization and mixed H∞and passivity control for memristive neural networks by using event-triggered ***,a switching system is constructed under the event-triggered control ***,by adopting a piece-wise Lyapunov functional,a sufficient condition is established for the exponential synchronization and mixed H_(∞)and passivity ***,an event-triggered controller design scheme is proposed using matrix decoupling ***,the effectiveness of the designed controller is exemplified by a numerical example.
Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks,...
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Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks, this paper employs a potential game for the vertex cover problem, designs a novel cost function for network vertices, and proves that the solutions to the minimum value of the potential function are the minimum vertex covering(MVC) states of a general complex network. To achieve the optimal(minimum) covering states, we propose a novel distributed time-variant binary log-linear learning algorithm,and prove that the MVC state of a general complex network is attained under the proposed optimization algorithm. Furthermore, we estimate the upper bound of the convergence rate of the proposed algorithm,and show its effectiveness and superiority using numerical examples with representative complex networks and optimization algorithms.
THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the developmen...
THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the development of sustainable agriculture, where a fundamental step is crop breeding to improve agronomic or economic traits, e.g., increasing yields of crops while decreasing resource usage and minimizing pollution to the environment [2].
In this paper,the problem of online distributed optimization subject to a convex set is studied via a network of *** agent only has access to a noisy gradient of its own objective function,and can communicate with its...
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In this paper,the problem of online distributed optimization subject to a convex set is studied via a network of *** agent only has access to a noisy gradient of its own objective function,and can communicate with its neighbors via a *** handle this problem,an online distributed stochastic mirror descent algorithm is *** works on online distributed algorithms involving stochastic gradients only provide the expectation bounds of the *** from them,we study the high probability bound of the regrets,i.e.,the sublinear bound of the regret is characterized by the natural logarithm of the failure probability's *** mild assumptions on the graph connectivity,we prove that the dynamic regret grows sublinearly with a high probability if the deviation in the minimizer sequence is sublinear with the square root of the time ***,a simulation is provided to demonstrate the effectiveness of our theoretical results.
This paper studies a geometric attitude tracking control problem of quadrotor unmanned aerial vehicles (UAVs) with adaptive extended state observers (AESOs). Through coordinate transformation, the error dynamic of AES...
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This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors...
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This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.
Accelerated MRI involves a trade-off between sampling sufficiency and acquisition time. Supervised deep learning methods have shown great success in MRI reconstruction from under-sampled measurements, but they typical...
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Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,***,the number of vehicle...
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Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,***,the number of vehicles participating in the traffic-in other words,the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management-is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network *** tackle this gap,an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment *** optimally scaling the Origin-Destination matrices of the sample fleet,an appropriate model can be approximated to provide traffic flow data beside average *** iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic *** method has been tested through two different real-world traffic networks,justifying the viability of the proposed ***,the contribution of the study is a practical solution based on commonly available fleet traffic data,suggested for practitioners in traffic planning and management.
In stochastic dynamic environments, multi-agent Markov decision processes have emerged as a versatile paradigm for studying sequential decision-making problems of fully cooperative multi-agent systems. However, the op...
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