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Generally speaking, controllability of multi-agent systems refer to transferring the remaining agents of such system from any arbitrary initial state to any final state by controlling dynamics of a small amount of agents under exchanged information between each other, which makes the system reflect the effect of a whole, that is the complete controllability. Comparing with the complete controllability...
Incompleteness in interval-valued information systems has leaded to many difficulties in acquiring knowledge. Rough set theory, proposed by Pawlak, has been widely used to deal with uncertainty, granularity and incompleteness of information. In this paper, we propose a θ-rough model for incomplete interval-valued information systems based on tolerance-similarity relation. Moreover, the accuracy, roughness...
Deep Convolutional Neural Network (CNN) is one of the most popular methods for image processing and recognition. There are many research works to improve the performance of CNNs. However, as an important part of CNNs, convolution kernel has rarely been discussed. As one Original Convolution Kernel (OCK) can only detect one type of visual feature with a fixed deformation, the networks using OCKs may...
Event-triggered circle formation control problems of multi-agent systems are investigated in this paper. We assume all the agents move on a given circle and each agent can only sense the relative positions of the two agents that are immediately in front of or behind itself. The circle formation control problem is to design distributed control laws such that all the agents asymptotically converge to...
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