In this paper, we investigate exponential stability of delayed recurrent neural networks. By using the delay partitioning method, some sufficient conditions are established to guarantee exponential stability of delaye...
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In this paper, we investigate exponential stability of delayed recurrent neural networks. By using the delay partitioning method, some sufficient conditions are established to guarantee exponential stability of delayed recurrent neural networks under two different conditions with constructing new Lyapunov–Krasvoskii functional. This partitioning approach can reduce the conservatism comparing with some previous results of stability. At last, numerical examples are given out to show the effectiveness and advantage of our results.
This paper is concerned with the problems of stability for a class of impulsive positive systems. An impulsive positive system model is introduced for the first time and a necessary and sufficient condition guaranteei...
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This paper is concerned with the problems of stability for a class of impulsive positive systems. An impulsive positive system model is introduced for the first time and a necessary and sufficient condition guaranteeing the positivity of this kind of system is proposed. Several sufficient criteria of global exponential stability and global asymptotical stability for impulsive positive systems are established respectively by using a linear copositive Lyapunov function. Two numerical examples are given to illustrate the effectiveness and applicability of the proposed results.
A novel statistical method using path integral Monte Carlo simulation based on quantum mechanics to detect edges of interested objects was proposed in this paper. Our method was inspired by essential characteristics o...
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作者：
Guang-Song HanZhi-Hong GuanJie ChenDing-Xin HeMing ChiCollege of Automation
Huazhong University of Science and Technology Wuhan 430074 China and the Key Laboratory of Image Information Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education Wuhan 430074 China
A multi-tracking problem of multi-agent networks is investigated in this paper where multi-tracking refers to that the states of multiple agents in each subnetwork asymptotically converge to the same desired trajector...
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A multi-tracking problem of multi-agent networks is investigated in this paper where multi-tracking refers to that the states of multiple agents in each subnetwork asymptotically converge to the same desired trajectory in the presence of information exchanges among *** multi-tracking of first order multi-agent networks with directed topologies was ***-triggered protocols were proposed along with triggering functions to solve the stationary multi-tracking and bounded dynamic *** self-triggered scheduling is obtained, and the system does not exhibit Zeno *** examples are provided to illustrate the effectiveness of the obtained criteria.
In this paper, we propose an Activity-List based Nested Partitions algorithm for solving the Resource-Constrained Project Scheduling Problem(RCPSP). This algorithm is based on traditional Serial Scheduling scheme (SSS...
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In this paper, we propose an Activity-List based Nested Partitions algorithm for solving the Resource-Constrained Project Scheduling Problem(RCPSP). This algorithm is based on traditional Serial Scheduling scheme (SSS) and partitions the feasible solution space which is formulated by activity-lists into subregions by the nested partitions approach. We also utilize Double Justification as local search to improve the solutions. The algorithm is tested on J120 in PSPLIB with the result that the algorithm is relatively effective for solving large-scale, complex RCPSPs.
A 5-kW dynamic solid oxide fuel cell(SOFC)system with a second air bypass has been developed with a model to perform both steady-state and dynamic analysis in this *** identifies and addresses the control challenges a...
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A 5-kW dynamic solid oxide fuel cell(SOFC)system with a second air bypass has been developed with a model to perform both steady-state and dynamic analysis in this *** identifies and addresses the control challenges associated with simultaneous power and thermal management under current-based control.
Inspired by the indoor localization technology of mobile robots, a novel Electronic Travel Aids (ETA) is developed based on multi-sensor fusion using an extended Kalman filter (EKF). A wearable sensor system is design...
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Inspired by the indoor localization technology of mobile robots, a novel Electronic Travel Aids (ETA) is developed based on multi-sensor fusion using an extended Kalman filter (EKF). A wearable sensor system is designed to estimate the real-time posture of user's lower limb. This system can be used both for monitoring the user's walking status and for realizing the relative localization. A belt of multiple sonar sensors worn by the user can detect the obstacle of unknown environment. Simultaneously, this sonar belt can also be used as the absolute localization device in a similar way as the mobile robot. An EKF is applied to realize the data fusion of different sensor systems. The abilities of obstacle avoidance and indoor localization of the proposed ETA are both verified through experiments.
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide...
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.
作者：
Sun, YangguangCai, ZhihuaCollege of Computer Science
South-Central University for Nationalities Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Software Engineering
Wuhan University College of Computer Science South-Central University for Nationalities Wuhan 430072 China
For the structural characteristics of Chinese NvShu character, by combining the basic idea in LLT local threshold algorithm and introducing the maximal betweenclass variance algorithm into local windows, an improved c...
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