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	Dynamic System Identification of Underwater Vehicles Using Multi-output Gaussian Processes 期刊论文 OAI收割 International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 681-693 作者: Wilmer Ariza Ramirez \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2021/09/13 Dependent Gaussian processes dynamic system identification multi-output Gaussian processes non-parametric identification autonomous underwater vehicle (AUV)
	A New Method for Characterization of Single Cell Using System Identification 会议论文 OAI收割 Xiamen, China, April 25-29, 2021 作者: Ma S(马爽); Wang WX(王文学); Wang YC(王越超); Liu LQ(刘连庆); Wang TL(王天路) \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2021/09/29 single cell dynamic mechanical properly Atomic Force Microscopy system identification
	Weighting by Cross-Validation: A Calibration Method for Force Measurements via Transient Response Analysis 期刊论文 OAI收割 EXPERIMENTAL TECHNIQUES, 2019, 卷号: 43, 期号: 4, 页码: 469-478 作者: Luo ZT(罗长童); Wang YP(汪运鹏); Hu ZM(胡宗民); Li JP(李进平); Jiang ZL(姜宗林) \| 收藏 \| 浏览/下载：39/0 \| 提交时间：2019/10/21 Aerodynamic force measurement Dynamic system Impact response Cross-validation System identification
	Data-Based Adaptive Critic Designs for Nonlinear Robust Optimal Control With Uncertain Dynamics 期刊论文 OAI收割 IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 卷号: 46, 期号: 11, 页码: 1544-1555 作者: Wang, Ding; Liu, Derong; Zhang, Qichao; Zhao, Dongbin \| 收藏 \| 浏览/下载：29/0 \| 提交时间：2017/02/14 Adaptive Critic Designs Adaptive Dynamic Programming Intelligent Control Neural Networks Policy Iteration Robust Optimal Control System Identification Uncertain Nonlinear Systems
	Research on the identification for a nonlinear system (EI CONFERENCE) 会议论文 OAI收割 International Conference on Optical, Electronic Materials and Applications 2011, OEMA 2011, March 4, 2011 - March 6, 2011, Chongqing, China 作者: Liu J.; Jia P.; Liu J.; Liu J. 收藏 \| 浏览/下载：31/0 \| 提交时间：2013/03/25 The characteristic of the drift error of inertial platform is a high-order nonlinear dynamic system using the neural networks' abilities of universal approximation of differentiable trajectory and capturing system dynamic information this paper presents the drift error identifying project of inertial platform based on Elman networks structure. First the drift error model of inertial platform is established after selecting the input and output for network momentum and alterable speed algorithm is used to speed up the network convergence. On the basis of the algorithm the extended nonlinear node function in the hidden network does not only improve the learning speed of network but also satisfies the need of accuracy on system identification. Through the drift error data measured on inertial platform the training result shows that the scheme achieves satisfied identification results. (2011) Trans Tech Publications.
	Study on navigation control method for CyberCar based on machine vision (EI CONFERENCE) 会议论文 OAI收割 2007 IEEE International Conference on Robotics and Biomimetics, ROBIO, December 15, 2007 - December 18, 2007, Yalong Bay, Sanya, China Zhang R.-H.; Wang R.-B.; You F.; Jia H.-G.; Chen T. 收藏 \| 浏览/下载：27/0 \| 提交时间：2013/03/25 The guiding principle and composition of CyberCar based on machine vision was introduced. Applying IM sequence signals as input response signals and least squares method to establish the dynamic equation for CyberCar steering system by system identification experiments firstly and then combined with the preview kinematics model and two-degree steering dynamic model of vehicle. Therefore it can trace the path steadily and reliably. 2008 IEEE. the steering control mathematics model based on preview kinematics for CyberCar was established. And then the switching hyper plane is designed by applying the optimal control theory during the change of the curve curvature radius is little the curve tracking of the intelligent vehicle is carried out by adopting sliding variable structure controller. Aim at the parameter uncertainty of tire a vehicle steering system uncertain model was founded and analyzed. A H optimal controller is designed with the method of H control theory to solve the problem about model uncertainty. The simulation and experiment results show that the controller designed by the proposed method has good robustness and adaptability
	Platform and steady kalman state observer design for intelligent vehicle based on visual guidance (EI CONFERENCE) 会议论文 OAI收割 2008 IEEE International Conference on Industrial Technology, IEEE ICIT 2008, April 21, 2008 - April 24, 2008, Chengdu, China Rong-hui Z.; Rong-ben W.; Feng Y.; Hong-guang J.; Tao C. 收藏 \| 浏览/下载：20/0 \| 提交时间：2013/03/25 State observer design is one of key technologies in research field of intelligent vehicle. Experiment platform visual guidance intelligent vehicle JLUIV-5 is establishedby Jilin University Intelligent Vehicle Group firstly. The system structure and assistant navigation control system and different image identify algorithms to recognize preview path and stops for variable illuminations are introduced. The dynamic response equation of steering control system was got by system identification experiment. By combined with the preview kinematics model and two-degree steering dynamic model of vehicle the steering kalman filter mathematics model based on preview kinematics for intelligent vehicle was obtained. And observer is designed by applying steady Kalman filter theory. The simulation and experiment results carry out in Jilin University Nanling Campus and Culture Center of Jilin Province show that the image identify algorithms and steady Kalmanstate observer designed by the proposed method has good adaptability for time-varying and parameters uncertain it can satisfy intelligent vehicle trace the path reliably during outdoor experiment. 2008 IEEE.