AADL (Architecture Analysis Design Language) is a standardized and hierarchical modeling language which contributes to designing and analyzing architectures of both software and hardware of Embedded Real-Time Systems....
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This paper develops a vector Lyapunov function based approach to the stability of continuous and discrete 2D nonlinear systems with Markovian jumps. Nonlinear continuous-time 2D systems described by a Roesser model an...
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This paper develops a vector Lyapunov function based approach to the stability of continuous and discrete 2D nonlinear systems with Markovian jumps. Nonlinear continuous-time 2D systems described by a Roesser model and nonlinear discrete-time repetitive process with Markovian jumps are considered, for which global asymptotic stability in the mean square is defined and sufficient conditions for the existence of this property obtained in terms of stochastic vector Lyapunov functions. These conditions are then applied to iterative learning control design for a set of linear systems with jumps that are controlled over a network. The resulting networked ILC model has the form of repetitive process with a Markovian random structure. The ILC convergence problem is reduced to a stochastic stabilization problem, where the use of a stochastic Lyapunov function as a vector of quadratic forms results in control law design algorithms that can be computed using linear matrix inequalities.
This paper introduces a fault tolerant controller design for nonlinear unknown systems with multiple actuators and bounded disturbance. The controller consists of an adaptive learning-based control law and a switching...
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Breast region segmentation is an essential prerequisite in the (semi-)automatic analysis of digital or digitised mammographic images, which aims to separate the breast region from background information in mammograms....
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There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced se...
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There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.
In this paper, a novel subspace algorithm entitled Simplified Nearest Feature Line Space (SNFLS) is proposed based on Nearest Feature Line (NFL). NFL space (NFLS) is a subspace learning method and has desirable discri...
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Identification and analysis of tissue-specific (TS) genes and their regulatory activities play an important role in the understanding of mechanisms of organisms, disease diagnosis and drug design. In this paper, we de...
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On the basis of studying the standard TCP retransmission mechanism, this paper proposes a method to adopt the theory of adaptive filtering in the field for the estimation of round trip time （RTT）. Then this method i...
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On the basis of studying the standard TCP retransmission mechanism, this paper proposes a method to adopt the theory of adaptive filtering in the field for the estimation of round trip time （RTT）. Then this method is embedded in TCP for estimation of RTT, and defined as RTT-AF TCP. In the schemes with Only-TCP flow and with TCP and UDP flow fighting for the link, the experiment results were made and analyzed by using the standard TCP and the RTT-AF TCP. The experimental results show higher throughput and lower loss rate of packets by using RTT-AF TCP for data transmission, and achieve excellent effect.
The current state-of-the-art machine intelligence cannot meet the challenges of uncertainty, complexity, time urgency and rapidly changing nature of the dynamic environment in many demanding aerospace applications, su...
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In this paper, we consider a two-dimensional (2-D) formation problem for multi-agent systems subject to switching topologies that dynamically change along both a finite time axis and an infinite iteration axis. We pre...
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ISBN:
(纸本)9781479901777
In this paper, we consider a two-dimensional (2-D) formation problem for multi-agent systems subject to switching topologies that dynamically change along both a finite time axis and an infinite iteration axis. We present a distributed iterative learning control (ILC) algorithm via the nearest neighbor rules. By employing the 2-D approach, we develop both the asymptotic and exponentially fast convergence of our formation ILC, which can be guaranteed by conditions in terms of the spectral radius and the matrix norms, respectively.
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