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	Power load data preprocessing based on PFCM algorithm and neural network 会议论文 Chongqing, China, May 24, 2019 - May 26, 2019 作者: Hao, Xiaohong; Zhang, Chunyan; Li, Hang; Wang, Weizhou; Liu, Fuchao 收藏 \| 浏览/下载：1/0 \| 提交时间：2020/11/15 Classification (of information) Curve fitting Electric load dispatching Electric power plant loads Electric power transmission networks Genetic algorithms Radial basis function networks Statistics Average relative error Clustering results Genetic-algorithm optimizations Number of clusters Outliers Power load RBF Neural Network Trained neural networks
	An unsupervised parameter learning model for RVFL neural network 期刊论文 NEURAL NETWORKS, 2019, 卷号: 112 作者: Zhang, Yongshan; Wu, Jia; Cai, Zhihua; Du, Bo; Yu, Philip S. 收藏 \| 浏览/下载：7/0 \| 提交时间：2019/12/05 Random vector functional link network Randomized feedforward neural networks Autoencoder l(1)-norm regularization Pre-trained parameters Classification applications
	Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification 期刊论文 REMOTE SENSING, 2017, 卷号: 9, 期号: 8 作者: Han, Xiaobing; Zhong, Yanfei; Cao, Liqin; Zhang, Liangpei 收藏 \| 浏览/下载：5/0 \| 提交时间：2019/12/05 scene classification convolutional neural network pre-trained AlexNet spatial pyramid pooling side supervision high spatial resolution remote sensing imagery
	Comparisons of Word Representations for Convolutional Neural Network: An Exploratory Study on Tourism Weibo Classification 会议论文 2017 14TH INTERNATIONAL CONFERENCE ON SERVICES SYSTEMS AND SERVICES MANAGEMENT (ICSSSM), 2017-01-01 作者: Sun, Rui-Hong; Hao, Jinxing 收藏 \| 浏览/下载：2/0 \| 提交时间：2019/12/30 pre-trained word vector internal word vector convolutional neural network tourism Weibo
	Fracture design parameters of middle carbon steel in extra-low cycle bend torsion loading 会议论文 Xi'an, Shanxi, China, May 9, 2014 - May 11, 2014 作者: Duan, Hong-Yan; Zhang, Huan-Rong; Zheng, Ming; Wang, Xiao-Hong 收藏 \| 浏览/下载：1/0 \| 提交时间：2020/11/15 Carbon steel Fracture Network architecture Neural networks Extra-low cycle Middle carbon steels Neural network model Number of hidden neurons Performance of systems Prediction techniques Torsion fatigue Trained neural networks
	Application of ANN back-propagation for fracture design parameters of middle carbon steel in extra-low cycle bend torsion loading 会议论文 Wuhan, China, June 22, 2013 - June 23, 2013 作者: Duan, HongYan; Li, YouTang; Sun, ZhiJia; Zhang, YangYang 收藏 \| 浏览/下载：1/0 \| 提交时间：2020/11/15 Backpropagation Carbon steel Industrial engineering Mechanical engineering Network architecture Neural networks Torsional stress Extra-low cycle Middle carbon steels Neural network model Number of hidden neurons Performance of systems Prediction techniques Torsion fatigue Trained neural networks
	Double inverted pendulum control based on three-loop PID and improved BP neural network (EI CONFERENCE) 会议论文 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China Sang Y.; Fan Y.; Liu B. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 To deal with the defects of BP neural networks used in balance control of inverted pendulum such as longer train time and converging in partial minimum this article reaLizes the control of double inverted pendulum with improved BP algorithm of artificial neural networks(ANN) builds up a training model of test simulation and the BP network is 6-10-1 structure. Tansig function is used in hidden layer and PureLin function is used in output layer LM is used in training algorithm. The training data is acquried by three-loop PID algorithm. The model is learned and trained with Matlab calculating software and the simuLink simulation experiment results prove that improved BP algorithm for inverted pendulum control has higher precision better astringency and lower calculation. This algorithm has wide appLication on nonLinear control and robust control field in particular. 2011 IEEE.
	Application of ANN back-propagation for an alloy reinforced ceramics/metal composite under extra-low cycle bending fatigue loading 会议论文 Harbin, China, August 16, 2009 - August 19, 2009 作者: Rui, Zhi-Yuan; Duan, Hong-Yan; Lei, Chunli; Wei, Xing-Chun 收藏 \| 浏览/下载：4/0 \| 提交时间：2020/11/15 Approximation theory Backpropagation Bending (deformation) Ceramic materials Ceramic matrix composites Data flow analysis Design Fracture Powder metallurgy Approximate methods Artificial Neural Network Ceramic composites Cycle time Fracture design Hidden neurons Input parameter Low cycle bending Network training Neural network model Performance of systems Prediction techniques Three-layer Tip radius Trained neural networks Training data
	Application of ANN back-propagation for residual stress in an alloy reinforced ceramics/metal composite 会议论文 Harbin, China, August 16, 2009 - August 19, 2009 作者: Duan, Hong-Yan; Li, You-Tang; Lei, Chunli; He, Guiping 收藏 \| 浏览/下载：1/0 \| 提交时间：2020/11/15 Approximation theory Backpropagation Ceramic materials Ceramic matrix composites Data flow analysis Forecasting Neural networks Powder metallurgy Residual stresses Thermal stress AL2O3/A356 CMCs Approximate methods Artificial Neural Network Ceramic composites Error evaluation Experimental data Mean relative error Performance of systems Prediction techniques Residual strains Thermal expansion behavior Trained neural networks Training model
	A method of aircraft image target recognition based on modified PCA features and SVM (EI CONFERENCE) 会议论文 9th International Conference on Electronic Measurement and Instruments, ICEMI 2009, August 16, 2009 - August 19, 2009, Beijing, China Donghe W.; Xin H.; Wei Z.; Huilong Y. 收藏 \| 浏览/下载：22/0 \| 提交时间：2013/03/25 Automatic target recognition(ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: Dimensionality reduction and Classifier. After pretreatment on original features a self-organizing neural network trained with the Hebbian rule is used to extract the principal component features. Then a classifier based on Directed Acyclic Graph Support Vector Machines(DAGSVM) is adopted to recognize more than two types of aircraft targets. The experiment results show the proposed method achieves better subset features and higher recognition rate. 2009 IEEE.

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