A5029038773- Neural Networks Stability and Synchronization
- Stability and Control of Uncertain Systems
- Advanced Memory and Neural Computing
- Distributed Control Multi-Agent Systems
- Neural Networks and Applications
- stochastic dynamics and bifurcation
- Chaos control and synchronization
- Adaptive Control of Nonlinear Systems
- Energy Load and Power Forecasting
- Nonlinear Dynamics and Pattern Formation
- Control Systems and Identification
- Matrix Theory and Algorithms
- Energy, Environment, Economic Growth
- Stability and Controllability of Differential Equations
- Air Quality Monitoring and Forecasting
- EEG and Brain-Computer Interfaces
- Machine Fault Diagnosis Techniques
- Evolutionary Game Theory and Cooperation
- ECG Monitoring and Analysis
- Air Quality and Health Impacts
- Distributed Sensor Networks and Detection Algorithms
- Control and Stability of Dynamical Systems
- Reinforcement Learning in Robotics
- Insurance, Mortality, Demography, Risk Management
- Solar Radiation and Photovoltaics
Wuhan University of Science and Technology
2015-2025
Wuchang University of Technology
2021
Wuhan University of Technology
2021
University of Kansas
2017
Huazhong University of Science and Technology
2008-2014
This paper investigates the finite-time distributed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> filtering problem in sensor networks with switching topology and two-channel stochastic attacks. The deception attacks are introduced, encompassing network communication channel between model sensor, as well sensors, implemented through an independent Bernoulli process...
A control strategy for stochastic nonlinear systems with time-varying high-order dynamics is presented in this paper, which specifically designed prescribed-time state-feedback stabilisation. The approach, based on the backstepping design, constructs a controller, utilising scaled quartic Lyapunov functions and given scaling function not used coordinate transformation. fundamental characteristic that system has unique strong solution, almost surely, implemented arbitrary initial conditions....
ABSTRACT This study delves into the distributed finite‐time extended dissipative filtering problem and event‐triggered strategy for nonlinear systems over sensor networks with two‐channel stochastic deception attacks. A new filter is proposed, which simultaneously addresses attack scenario. Here, devised in a manner, model following Bernoulli distribution established. Additionally, index considered to resolve , passive, within unified framework. Sufficient conditions are derived ensure...
Daily weather conditions are closely related to every field of production and life, the forecasting plays an important role in social development. Based on data characteristics urban conditions, a deep learning network was designed forecast its feasibility proved by experiments. In view non-stationary seasonal fluctuation time series daily Shenzhen from 2015 2019, empirical mode decomposition (EMD) used carry out stationary processing for minimum humidity, pressure, maximum temperature, wind...
Overfitting often occurs in neural network training, and networks with higher generalization ability are less prone to this phenomenon. Aiming at the problem that of photovoltaic (PV) power prediction model is insufficient, a PV time-sharing (TSP) combining variational mode decomposition (VMD) Bayesian regularization (BRNN) proposed. Firstly, meteorological sequences related output selected by mutual information (MI) analysis. Secondly, VMD processing performed on filtered sequences, which...