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	Neural-Adaptive Finite-Time Formation Tracking Control of Multiple Nonholonomic Agents with a Time-Varying Target 期刊论文 OAI收割 IEEE Access, 2020, 卷号: 8, 页码: 62943-62953 作者: Zhou, Kai-Bo; Wu, Xiao-Kang; Ge, Ming-Feng; Liang, Chang-Duo; Hu, Bing-Liang \| 收藏 \| 浏览/下载：28/0 \| 提交时间：2020/05/19 Multiple nonholonomic agent system (MNAS) leader-following formation tracking problem (FTP) radial basis function (RBF) neural network distributed controller-estimator algorithm(DCEA)
	Recovery of Collided RFID Tags With Frequency Drift on Physical Layer 期刊论文 OAI收割 IEEE/CAA Journal of Automatica Sinica, 2020, 卷号: 7, 期号: 6, 页码: 1593-1603 作者: Junzhi Li; Haifeng Wu; Yu Zeng \| 收藏 \| 浏览/下载：31/0 \| 提交时间：2021/03/11 Frequency drift radial basis function (RBF) radio frequency identification (RFID) separation efficiency tag collision
	A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation 期刊论文 OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 11, 页码: 3433-3443 作者: Luo, Lincong; Peng, Liang; Wang, Chen; Hou, Zeng-Guang \| 收藏 \| 浏览/下载：40/0 \| 提交时间：2020/03/30 Medical treatment Training Task analysis Robot sensing systems Impedance Trajectory Assist as needed (AAN) challenge level Gaussian radial basis function (RBF) network motor capability rehabilitation robot upper limb
	Generalization Performance of Radial Basis Function Networks 期刊论文 OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 卷号: 26, 期号: 3, 页码: 551-564 作者: Lei, Yunwen; Ding, Lixin; Zhang, Wensheng 收藏 \| 浏览/下载：32/0 \| 提交时间：2015/11/08 Learning theory local Rademacher complexity radial basis function (RBF) networks structural risk minimization (SRM)
	Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (EI CONFERENCE) 会议论文 OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者: Gao X. 收藏 \| 浏览/下载：22/0 \| 提交时间：2013/03/25 Support Vector Machine (SVM) is used to classify hyperspectral remote sensing image in this paper. Radial Basis Function (RBF) which is most widely used is chosen as the kernel function of SVM. Selection of kernel function parameter is a pivotal factor which influences the performance of SVM. For this reason Particle Swarm Optimization (PSO) is provided to get a better result. In order to improve the optimization efficiency of kernel function parameter firstly larger steps of grid search method is used to find the appropriate rang of parameter. Since the PSO tends to be trapped into local optimal solutions a weight and mutation particle swam optimization algorithm was proposed in which the weight dynamically changes with a liner rule and the global best particle mutates per iteration to optimize the parameters of RBF-SVM. At last a 220-bands hyperspectral remote sensing image of AVIRIS is taken as an experiment which demonstrates that the method this paper proposed is an effective way to search the SVM parameters and is available in improving the performance of SVM classifiers. 2012 IEEE.
	An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection 期刊论文 OAI收割 IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2011, 卷号: 41, 期号: 1, 页码: 107-117 作者: Xu, Yanwu; Cao, Xianbin; Qiao, Hong 收藏 \| 浏览/下载：23/0 \| 提交时间：2015/08/12 Efficient classification false-positive rate (FPR) pedestrian detection performance evaluation radial basis function (RBF) neural network.
	图像序列中的光流估计与运动分割学位论文 OAI收割工学硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2010 马东民收藏 \| 浏览/下载：52/0 \| 提交时间：2015/09/02 统计学习模型马尔科夫随机场 RBF模型约束函数选择运动分割图像序列 statistical modeling markov random field radial basis function neural network motion selection motion segmentation image sequence
	Space camera focusing forecast based on RBF network (EI CONFERENCE) 会议论文 OAI收割 Computational Intelligence and Intelligent Systems: 4th International Symposium, ISICA 2009, Huangshi, China, October 23-25, 2009. Proceedings, Tiergartenstrasse 17, Heidelberg, D-69121, Germany 作者: Liu J.; Yu D.; Yu D.; Liu J.; Liu J. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 As circumstance temperature of space camera changes flex of structural components and distortion of optical components lead to change of focal length and image quality. Radial Basis Function (RBF) network is used to approximate the complex nonlinear relation between focalization quantity image quality temperature level and axial temperature difference of space camera. After the RBF Network is trained with thermo-optical experiment data temperature level and axial temperature difference could be input to the network to obtain colder value of best image position. In this way focusing forecast under different temperatures can be realized. Results of focusing forecast experiment validate this method. 2009 Springer-Verlag Berlin Heidelberg.