The time series of landslide displacement reflects the evolution of landslide, and is monotonously increasing and nonlinear. With ensemble empirical mode decomposition (EEMD) methods the monitored displacement series ...
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ISBN:
(纸本)9781479900305
The time series of landslide displacement reflects the evolution of landslide, and is monotonously increasing and nonlinear. With ensemble empirical mode decomposition (EEMD) methods the monitored displacement series is decomposed into relatively simple components, then BP neural network was applied to forecasting the evolution of each component, and the individual predictions were summed to get the final result of prediction. 3-dimensional modeling software is adopted to plot the 3-dimensional terrain model of the monitored areas, based on the data collected from these areas. Finally, with the combination of mathematical models for landslide prediction and 3-d terrain model, the visual simulation of landslide evolution is realized in visualizing software.
This paper investigates the adaptive synchronization of complex dynamical networks satisfying the local Lipschitz condition with switching topology. Based on differential inclusion and nonsmooth analysis, it is proved...
This paper investigates the adaptive synchronization of complex dynamical networks satisfying the local Lipschitz condition with switching topology. Based on differential inclusion and nonsmooth analysis, it is proved that all nodes can converge to the synchronous state, even though only one node is informed by the synchronous state via introducing decentralized adaptive strategies to the coupling strengths and feedback gains. Finally, some numerical simulations are worked out to illustrate the analytical results.
The prediction of landslide displacement is essential for carrying out to improve the disaster warning system and reduce casualties and property losses. This study applies a novel neural network technique, extreme lea...
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The prediction of landslide displacement is essential for carrying out to improve the disaster warning system and reduce casualties and property losses. This study applies a novel neural network technique, extreme learning machine (ELM) with kernel function, to landslide displacement prediction problem. However, the generalization performance of ELM with kernel function depends closely on the kernel types and the kernel parameters. In this paper, we use a convex combination of Gaussian kernel function and polynomial kernel function in ELM, which may use these two types of kernel functions' advantages. In order to avoid blindness and inaccuracy in parameter selection, a novel hybrid optimization algorithm based on the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) is used to optimize the regularization parameter C, the Gaussian kernel parameter γ, the polynomial kernel parameter q and the mixing weight coefficient η. The performance of our model is verified through two case studies in Baishuihe landslide and Yuhuangge landslide.
This paper investigates controllability of discrete-time multi-agent systems with multiple leaders on fixed networks. The leaders are particular agents playing a part in external inputs to steer other member agents. T...
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This paper investigates controllability of discrete-time multi-agent systems with multiple leaders on fixed networks. The leaders are particular agents playing a part in external inputs to steer other member agents. The followers can arrive at any predetermined configuration by regulating the behaviors of the leaders. Some sufficient and necessary conditions are proposed for the controllability of discrete-time multi-agent systems with multiple leaders. Moreover, the case with isolated agents is discussed. Numerical examples and simulations are proposed to illustrate the theoretical results we established.
Membrane computing is an emergent branch of natural computing, which is inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order st...
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Membrane computing is an emergent branch of natural computing, which is inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. Tissue P systems are a class of the most investigated computing mod- els in the framework of membrane computing, especially in the aspect of efficiency. To generate an exponential resource in a polynomial time, cell separation is incorporated into such systems, thus obtaining so called tissue P systems with cell separation. In this work, we exploit the computational efficiency of this model and construct a uniform family of such tissue P systems for solving the independent set problem, a well-known NP-complete problem, by which an efficient so- lution can be obtained in polynomial time.
In this study, the sliding mode approach is applied to the tracking control problem of a planar arm manipulator system driven by a new type of actuator, which comprises a pneumatic muscle (PM) and a torsion spring. Un...
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In this study, the sliding mode approach is applied to the tracking control problem of a planar arm manipulator system driven by a new type of actuator, which comprises a pneumatic muscle (PM) and a torsion spring. Unlike the traditional agonist/antagonist PM actuator, the PM is arranged in place of bicep and the torsion spring provides opposing torque in the presented actuator. The dynamic model is derived for this system and a sliding mode controller is designed to make the joint angle track a desired trajectory within a guaranteed accuracy even there are modelling uncertainties. A selection method is also proposed to obtain an appropriate spring coefficient, which plays an important role in the tracking control task. The effectiveness of the proposed method is confirmed by simulations. The differences between the control results of using our new actuator and that of using the traditional PM actuator in opposing pair configuration are also compared.
作者：
Huan ChengXi LiJianhua JiangLin ZhangJian LiJie YangDepartment of control science and Engineering
Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China School of Materials Science and Engineering State Key Laboratory of Material Processing and Die and Mould Technology Huazhong University of Science and Technology Wuhan 430074 China School of Mechanical and Electronic Information China University of Geosciences Wuhan 430074 China
In the literature （Tan and Wang, 2010）, Tan and Wang investigated the convergence of the split-step backward Euler （SSBE） method for linear stochastic delay integro-differential equations （SDIDEs） and proved the...
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In the literature （Tan and Wang, 2010）, Tan and Wang investigated the convergence of the split-step backward Euler （SSBE） method for linear stochastic delay integro-differential equations （SDIDEs） and proved the mean-square stability of SSBE method under some condition. Unfortu- nately, the main result of stability derived by the condition is somewhat restrictive to be applied for practical application. This paper improves the corresponding results. The authors not only prove the mean-square stability of the numerical method but also prove the general mean-square stability of the numerical method. Furthermore, an example is given to illustrate the theory.
In this brief, we investigate pinning control for cluster synchronization of undirected complex dynamical networks using a decentralized adaptive strategy. Unlike most existing pinning-control algorithms with or witho...
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In this brief, we investigate pinning control for cluster synchronization of undirected complex dynamical networks using a decentralized adaptive strategy. Unlike most existing pinning-control algorithms with or without an adaptive strategy, which require global information of the underlying network such as the eigenvalues of the coupling matrix of the whole network or a centralized adaptive control scheme, we propose a novel decentralized adaptive pinning-control scheme for cluster synchronization of undirected networks using a local adaptive strategy on both coupling strengths and feedback gains. By introducing this local adaptive strategy on each node, we show that the network can synchronize using weak coupling strengths and small feedback gains. Finally, we present some simulations to verify and illustrate the theoretical results.
Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses (called "spikes"). In this paper, we co...
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Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses (called "spikes"). In this paper, we continue the research of normal forms for spiking neural P systems. Specifically, we prove that the degree of spiking neural P systems without delay can be decreased to two without losing the computational completeness (both in the generating and accepting modes).
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