作者：
Guiyuan FuWeidong ZhangDepartment of Automation
Shanghai Jiaotong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 P. R. China
The continuous opinion dynamics with group-based heterogeneous bounded confidences is considered in this paper. Firstly, a slightly modified Hegselmann-Krause model is proposed, and the agents are divided into open-mi...
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The continuous opinion dynamics with group-based heterogeneous bounded confidences is considered in this paper. Firstly, a slightly modified Hegselmann-Krause model is proposed, and the agents are divided into open-minded-, moderate-minded-, and close-minded-subgroups according to the corresponding confidence intervals. Then numerical simulations are carried out to analyze the influence of the close-minded and open-minded agents, as well as the population size, on the opinion dynamics. It is observed that (1) for the fixed population size, the larger proportion of close-minded agents, the more opinion clusters; (2) open-minded agents cannot contribute to forging different opinions, instead, the existence of them maybe diversify final opinions; also interestingly the relative size of the largest cluster varies along concave-parabola-like curve as the proportion of open-minded agents increases; (3) for the same proportion of the three subgroups, as population size increases, the number of final opinion clusters will increase at the beginning and then reach a stable level, which is quite different from the previous studies.
Interval-valued data and incomplete data are two key problems for failure analysis of thruster experimental data and have been basically solved by the proposed methods in this paper. Firstly, information data acquired...
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Interval-valued data and incomplete data are two key problems for failure analysis of thruster experimental data and have been basically solved by the proposed methods in this paper. Firstly, information data acquired from the simulation and evaluation system formed as intervalvalued informationsystem （IIS） is classified by the interval similarity relation. Then, as an improvement of the classical rough set, a new kind of generalized information entropy called ＂H＇-information entropy＂ is suggested for the measurement of uncertainty and the classification ability of IIS. There is an innovative information filling technique using the properties of H＇-information entropy to replace missing data by some smaller estimation intervals. Finally, an improved method of failure analysis synthesized by the above achievements is presented to classify the thruster experimental data, complete the information, and extract the failure rules. The feasibility and advantage of this method is testified by an actual application of failure analysis, whose performance is evaluated by the quantification of E-condition entropy.
This paper addresses the problem of infinite time performance of model predictive controllers applied to constrained nonlinear systems. The total performance is compared with a finite horizon optimal cost to reveal pe...
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Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. ...
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Many recent state-of-the-art image retrieval approaches are based on Bag-of-Visual-Words model and represent an image with a set of visual words by quantizing local SIFT（scale invariant feature transform） features. Feature quantization reduces the discriminative power of local features and unavoidably causes many false local matches between images, which degrades the retrieval accuracy. To filter those false matches, geometric context among visual words has been popularly explored for the verification of geometric consistency. However, existing studies with global or local geometric verification are either computationally expensive or achieve limited accuracy. To address this issue, in this paper, we focus on partialduplicate Web image retrieval, and propose a scheme to encode the spatial context for visual matching verification. An efficient affine enhancement scheme is proposed to refine the verification results. Experiments on partial-duplicate Web image search, using a database of one million images, demonstrate the effectiveness and efficiency of the proposed *** on a 10-million image database further reveals the scalability of our approach.
This paper deals with the consensus problem for heterogeneous multi-agent systems. Different from most existing consensus protocols, we consider the consensus seeking of two types of agents, namely, active agents and ...
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This paper deals with the consensus problem for heterogeneous multi-agent systems. Different from most existing consensus protocols, we consider the consensus seeking of two types of agents, namely, active agents and passive agents. The objective is to directly control the active agents such that the states of all the agents would achieve consensus. In order to obtain a computational approach, we subtly introduce an appropriate Markov chain to cast the heterogeneous systems into a unified framework. Such a framework is helpful for tackling the constraints from passive agents. Furthermore, a sufficient and necessary condition is established to guarantee the consensus in heterogeneous multi-agent systems. Finally, simulation results are provided to verify the theoretical analysis and the effectiveness of the proposed protocol.
In the brand new era of “big data”, finding creative strategies for information fusion in a complicated system is important. Previous researchers have introduced a model called “Local Strongly Coupled system”, in ...
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In the brand new era of “big data”, finding creative strategies for information fusion in a complicated system is important. Previous researchers have introduced a model called “Local Strongly Coupled system”, in which every agent stochastically communicates only with its neighbours (i.e. “coupled” nodes). However, the meaning of a system's intrinsic a priori constraints is always neglected. And we assume that in practical instances, the whole system is supposed to take on a consistent result, or “consensus” decision. This paper, taking constraints into account, presents a new strategy to help minimize the filtering error. Furthermore, based on consensus policy, the approach is applied to local strongly coupled systems, especially systems with packet loss. Effectiveness and practicability of all the proposed algorithms are shown through simulations.
Previously, we proposed a topological-based swarming model in which agents probabilistically decide which other agents to interact with based on the proximity of positions of agents. Agents then average their directio...
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Previously, we proposed a topological-based swarming model in which agents probabilistically decide which other agents to interact with based on the proximity of positions of agents. Agents then average their direction with the directions of the agents they have interacted with. In this paper, we improve the model mentioned-above by taking the influence of similarity of directions into consideration, and the former model can be considered as a special case of this model here. We propose a probabilistic method which depends on both proximity of agents' positions and similarity of agents' moving directions. And agents are more likely to form links with those agents who carry a similar direction to theirs and those agents who are proximal to them. We show that there exits a non-zero positive lower bound of the selection probability, and the system can be connected in probability, which ensures the system's achievement of swarming. And by simulations, it is shown that the rate of getting alignment exhibit a strong correlation with the parameters of the system which are weighting factor, neighborhood size, proximity factor and similarity factor.
作者：
Xiaofan WangXiaoling WangDepartment of Automation
Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China
Existed works on consensus in networks have been focused on reaching an agreement among states of nodes in a network. In this work, we propose a discrete-time edge consensus protocol for complex networks. By mapping t...
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ISBN:
(纸本)9781479934331
Existed works on consensus in networks have been focused on reaching an agreement among states of nodes in a network. In this work, we propose a discrete-time edge consensus protocol for complex networks. By mapping the edge topology into a corresponding line graph, we prove that consensus can be achieved among the states of all edges in a connected network. Theoretical analysis and simulation results are provided to show the effectiveness of the model and the influence of network topology.
Walking on irregular terrain is usually a common task for a quadruped robot. It is however difficult to control the robot in this situation as undesirable impulse force by collision between the foot of robot and obsta...
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This paper designs multi-step probabilistic sets for linear, discrete-time, stochastic systems with unbounded multiplicative noise and probabilistic constraints. Multi-step probabilistic sets strengthen IWPp by bringi...
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This paper designs multi-step probabilistic sets for linear, discrete-time, stochastic systems with unbounded multiplicative noise and probabilistic constraints. Multi-step probabilistic sets strengthen IWPp by bringing more degrees of freedom to optimize the applicable region of finite-step probabilistic constraints, and extending the prediction horizon of IWPp to infinity for infinite-horizon probabilistic constraints. Conditions for multi-step probabilistic sets are then incorporated into a stochastic model predictive control algorithm to satisfy probabilistic constraints. Closed-loop mean-square stability is guaranteed by the algorithm. A numerical example shows the performance of the proposed algorithm.
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