This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behavior...
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This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled sub- systems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results.
This paper addresses the problem of adaptive pinning synchronization of complex dynamical networks with nonlinear delayed intrinsic dynamics and time-varying delays. By introducing decentralized adaptive strategies to...
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An approach of direct adaptive fuzzy sliding-mode control which combines the fuzzy control with the sliding-mode control, is proposed for the control of a class of unknown nonlinear dynamic systems. The control goal i...
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ISBN:
(纸本)9781479987313
An approach of direct adaptive fuzzy sliding-mode control which combines the fuzzy control with the sliding-mode control, is proposed for the control of a class of unknown nonlinear dynamic systems. The control goal is to obtain a direct adaptive fuzzy sliding-mode control law and a constructive Lyapunov synthesis approach with respect to a class of nonlinear systems without the knowledge of uncertainties. For improving the approximate performance of the fuzzy system, the proposed approach in this study not only online updates the parameter values in the consequence fuzzy sets, but also updates the shape parameters of the membership functions of the prime fuzzy sets. The fuzzy control rules are updated through the online adaptive learning, which makes the output of fuzzy control to approximate to a sliding-mode equivalent control. The asymptotic stability of the overall system based on Lyapunov theory is proved. Some numerical simulation results show the efficiency of the proposed approach.
Spiking neural (SN) P systems are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes. In this work, we investigate reversibility in SN P systems, as well as ...
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Spiking neural (SN) P systems are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes. In this work, we investigate reversibility in SN P systems, as well as the computing power of reversible SN P systems. Reversible SN P systems are proved to have Turing creativity, that is, they can compute any recursively enumerable set of non-negative integers by simulating universal reversible register machine.
In this work, inspired from this biological motivation that in living cells, the execution time of different biological processes is difficult to know precisely and very sensitive to environmental factors that might b...
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This paper investigates the synchronization problem of memristive systems with multiple networked input and output delays via observer-based control. A memristive system is set up, and the fuzzy method has been employ...
This paper investigates the synchronization problem of memristive systems with multiple networked input and output delays via observer-based control. A memristive system is set up, and the fuzzy method has been employed to linearize the dynamical system of the memristive system; the networked input and output delays are considered in the synchronization problem of this system. A truncated predictor feedback approach is employed to design the observers. Under certain restrictions, a class of finite-dimensional observer-based output feedback controllers is designed. A numerical example is carried out to demonstrate the effectiveness of the proposed methods.
作者:
Li, JiaojieZhang, WeiSu, HoushengYang, YupuDepartment of Automation
Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Measurement and Control Technology
Shanghai Dian Ji University Shanghai China School of Automation
Key Laboratory of Image Information Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan China
In this paper, we study the flocking problem of multi-agent systems with obstacle avoidance, in the situation when only a fraction of the agents have information on the obstacles. Obstacles of arbitrary shape are allo...
In this paper, we study the flocking problem of multi-agent systems with obstacle avoidance, in the situation when only a fraction of the agents have information on the obstacles. Obstacles of arbitrary shape are allowed, no matter if their boundary is smooth or non-smooth, and no matter it they are convex or non-convex. A novel geometry representation rule is proposed to transfer obstacles to a dense obstacle-agents lattice structure. Non-convex regions of the obstacles are detected and supplemented using a geometric rule. The uninformed agents can detect a section of the obstacles boundary using only a range position sensor. We prove that with the proposed protocol, uninformed agents which maintain a joint path with any informed agent can avoid obstacles that move uniformly and assemble around a point along with the informed agents. Eventually all the assembled agents reach consensus on their velocity. In the entire flocking process, no distinct pair of agents collide with each other, nor collide with obstacles. The assembled agents are guaranteed not to be lost in any non-convex region of the obstacles within a distance constraint. Numerical simulations demonstrate the flocking algorithm with obstacle avoidance both in 2D and 3D space. The situation when every agent is informed is considered as a special case.
This paper is considered with the H ∞ observer design problem for a class of nonlinear systems with the one-sided Lipschitz condition. The systems under consideration include the well-studied Lipschitz system as a sp...
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This paper is considered with the H ∞ observer design problem for a class of nonlinear systems with the one-sided Lipschitz condition. The systems under consideration include the well-studied Lipschitz system as a special case and possess inherent advantages with respect to conservativeness. For such systems in the presence of noises, we develop a Linear Matrix Inequality (LMI) based approach to design a nonlinear H ∞ observer by carefully dealing with the one-sided Lipschitz condition together with the quadratic inner-bounded condition. The resulting nonlinear H ∞ observer guarantees asymptotic stability of the estimation error dynamics with a prescribed H ∞ performance. Moreover, for the design purpose, the existence condition of the proposed nonlinear H ∞ observer is formulated in terms of LMIs by using a matrix generalized inverse technique. Finally, a simulation example is given to illustrate the effectiveness of the proposed design.
This paper proposes two hybrid prediction models using for predicting the displacement of landslide, Genetic Algorithm-Radial Basis Function Neural Network (GA-RBFN) and Genetic Algorithm- Back Propagation Neural Netw...
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This paper proposes two hybrid prediction models using for predicting the displacement of landslide, Genetic Algorithm-Radial Basis Function Neural Network (GA-RBFN) and Genetic Algorithm- Back Propagation Neural Network (GA-BPNN). A case study of Yuhuangge landslide in the Three Gorges reservoir in China is used to illustrate the capability and merit of our schemes. In addition, the result shows that GP-BPNN get better accuracy than GA-RBFN in the same measurements.
This work investigates the conflict-free path planning problems for efficient guidance of multiple mobile robots under the dynamic double-warehouse environment, a challenging problem that appears recurrently in a wide...
This work investigates the conflict-free path planning problems for efficient guidance of multiple mobile robots under the dynamic double-warehouse environment, a challenging problem that appears recurrently in a wide range of applications such as the service robots moving in a multistory building. United with two symmetrical transfer elevators, double-warehouse consists of two parallel warehouses. Within each warehouse, the polynomial based paths are subject to constraints such as motion boundaries, kinematics constraints, obstacle-avoidance, limited resource of elevators and smoothness. We formulate the shortest path planning problems as one time-varying nonlinear programming problem (TNLPP) while restricted to the above constraints, and apply the multi-phase strategy to reduce their difficulty. We present the new variant algorithms of PSO named constriction factor and random perturb PSO (Con-Per-PSO) and the simulating annealing PSO (SA-PSO) to achieve the solution. Numerical simulations verify that, our approach can fulfill multiple mobile robots path planning problems under double-warehouse successfully.
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