Neural Processing Letters - Online sequential extreme learning machine (OS-ELM) is one of the most popular real-time learning strategy for feedforward neural networks with single hidden layer due... 相似文献
Multimedia Tools and Applications - Almost all existing image encryption algorithms are only suitable for low-resolution images in the standard image library. When they are used to encrypt... 相似文献
World Wide Web - With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life.... 相似文献
This paper proposes a sequential design scheme for switching ℌ∞ LPV (Linear Parameter-Varying) control, aiming to reduce the computational complexity of the associated optimization problem. Different from the traditional approach that simultaneously designs switching LPV controllers and solves a high-dimensional optimization problem, the proposed sequential design approach renders a bundle of low-dimensional optimization problems to be solved iteratively. Individual ℌ∞ LPV controller for each subregion is synthesized by independent PLMIs (Parametric Linear Matrix Inequalities) to guarantee ℌ∞ performance, and controller variables are interpolated on the overlapped subregions such that the ℌ∞ performance is also guaranteed on the overlapped subregion. Numerical examples are used to demonstrate the effectiveness of this method to reduce the computational load in each design iteration and improved ℌ∞ performance over the conventional simultaneous design method with well-tuned interpolation coefficient.
International Journal of Control, Automation and Systems - This paper presents a novel filtering based multi-innovation estimation algorithm for output-error autoregressive moving average (i.e.,... 相似文献
International Journal of Control, Automation and Systems - In this paper, we discuss actuator fault and sensor fault detection and isolation (FDI) problems for a class of switched nonlinear systems... 相似文献
Runoff simulation is highly significant for hydrological monitoring, flood peak simulation, water resource management, and basin protection. Runoff simulation by distributed hydrological models, such as the soil and water assessment tool (SWAT) model which is the most widely used, is becoming a hotspot for hydrological forecasting research. However, parameter calibration is inefficient and inaccurate for the SWAT model. An automatic parameter calibration (APC) method of the SWAT model was developed by hybrid of the genetic algorithm (GA) and particle swarm optimization (PSO). Multi‐station and multi‐period runoff simulation and accuracy analysis were conducted in the basin of the Zhangjiang River on the basis of this hybrid algorithm. For example, in the Yaoxiaba Station, the calibration results produced an R2 of 0.87 and Nash Sutcliffe efficiency (NSE) index of 0.85, while verification results revealed an R2 of 0.83 and NSE of 0.83. Results of this study show that the proposed method can effectively improve the efficiency and simulation accuracy of the model parameters. It can be concluded that the feasibility and applicability of GA‐PSO as an APC method for the SWAT model were confirmed via case studies. The proposed method can provide theoretical guidance for many hydrological research fields, such as hydrological simulation, flood prevention, and forecasting. 相似文献