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.
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.
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
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.
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.
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).
This paper addresses the stability and stabilization problems for a class of positive linear systems in the presence of saturating actuators. The objective is to give conditions of the stability, and design state feed...
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This paper addresses the stability and stabilization problems for a class of positive linear systems in the presence of saturating actuators. The objective is to give conditions of the stability, and design state feedback control laws such that the closed-loop systems is asymptotically stable and positive at the origin with a large domain of attraction. Several sufficient conditions for stabilization and positivity are derived via the Lyapunov functions method and convex analysis in both the continuous-time and the discrete-time cases, respectively. The state feedback controller design and the estimation of domain of attraction are presented by solving a convex optimization problem with LMIs constraints. A numerical example is given to show the effectiveness of the proposed methods.
Presently pneumatic muscles (PMs) are used in various applications due to their simple construction, lightweight and high force to weight ratio. However, pneumatic muscles are facing various problems due to their nonl...
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In this paper, an optimal guidance algorithm is proposed for atmospheric ascent. The optimal guidance algorithm updates the reference trajectory to deal with the impact of disturbance by solving an optimal control pro...
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In this paper, the Pneumatic Muscle (PM) as actuator is investigated. The PM model is established by using a phenomenological model consisting of a contractile element, a spring element, and a damping element in paral...
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