A5048492118- Neural Networks Stability and Synchronization
- Stability and Control of Uncertain Systems
- Distributed Control Multi-Agent Systems
- Chaos control and synchronization
- Nonlinear Dynamics and Pattern Formation
- Matrix Theory and Algorithms
- Adaptive Control of Nonlinear Systems
- Neural Networks and Applications
- Advanced Memory and Neural Computing
- Elasticity and Wave Propagation
- Ideological and Political Education
- Machine Learning and ELM
- stochastic dynamics and bifurcation
- Educational Technology and Pedagogy
- Direction-of-Arrival Estimation Techniques
- Fault Detection and Control Systems
- Fuzzy Logic and Control Systems
- Speech and Audio Processing
- Winter Sports Injuries and Performance
- Higher Education and Teaching Methods
- Advanced Image and Video Retrieval Techniques
- Sports Dynamics and Biomechanics
- Teleoperation and Haptic Systems
- Innovative Educational Techniques
- Underwater Vehicles and Communication Systems
North China University of Science and Technology
2016-2025
Yanshan University
2013-2025
North China University of Technology
2021-2024
Tangshan College
2024
Ningbo University
2024
Zhejiang University
2024
Dalian Maritime University
2024
Institute of Electrical Engineering
2013-2023
North China Electric Power University
2023
Xinjiang University
2021
This brief is concerned with the problem of asymptotic stability neural networks time-varying delays. The activation functions are monotone nondecreasing known lower and upper bounds. Novel criteria derived by employing new Lyapunov-Krasovskii functional integral inequality. developed have delay dependencies results characterized linear matrix inequalities. New less conservative solutions to global provided in terms feasibility testing. Numerical examples finally given demonstrate...
The asymptotical synchronization problem is investigated for two identical chaotic Lur'e systems with time delays. sampled-data control method employed the system design. A new condition proposed in form of linear matrix inequalities. error shown to be asymptotically stable constructed piecewise differentiable Lyapunov-Krasovskii functional (LKF). Different from existing work, LKF makes full use information nonlinear part system. obtained stability less conservative than some ones. longer...
The dissipative stability problem for a class of Takagi-Sugeno (T-S) fuzzy systems with variable sampling control is the focus this paper. controller signals are assumed to transmit constant delay. Our aim design sampled-data such that T-S system globally asymptotically stable (Q, S, R)-γ-dissipative performance index. analyzed by using novel piecewise Lyapunov-Krasovskii functional (LKF) together looped-functional and free-matrix-based (FMB) inequality method. First, several useful linear...
In this paper, we address the consensus tracking problem for multiagent system (MAS) based on a nonfragile memory sampled-data controller. Considering effect of controller gain fluctuation and communication delay, novel control scheme with variable sampling interval is designed each agent. By developing some new terms, an improved piecewise Lyapunov-Krasovskii functional (LKF) constructed to take full advantage characteristic about real pattern. Furthermore, relaxed matrices in LKF are not...
The synchronization for a class of switched uncertain neural networks (NNs) with frequent asynchronism based on event-triggered control is researched in this article. Compared existing works that require one switching during an inter-event interval, allowed By employing controller-mode-dependent Lyapunov–Krasovskii functionals (LKFs), we devise the strategy to guarantee NNs can be synchronized. proposed LKFs make full use system information. Using improved integral inequality, some...
Depth estimation is an important computer vision problem with many practical applications to mobile devices. While solutions have been proposed for this task, they are usually very computationally expensive and thus not applicable on-device inference. To address problem, we introduce the first Mobile AI challenge, where target develop end-to-end deep learning-based depth that can demonstrate a nearly real-time performance on smartphones IoT platforms. For this, participants were provided new...
Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of designed models available publicly up until now. To address problem, we introduce first Mobile AI challenge, where target to develop quantized deep learning-based camera classification that can demonstrate a real-time performance smartphones and IoT platforms. For this, participants provided with large-scale CamSDD dataset...