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	An improved hyperspectral classification algorithm based on back-propagation neural networks (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: Yu P.; Yu P. 收藏 \| 浏览/下载：34/0 \| 提交时间：2013/03/25 In this paper a new method is proposed to improve the classification performance of hyperspectral images by combining the principal component analysis (PCA) genetic algorithm (GA) and artificial neural networks (ANNs). First some characteristics of the hyperspectral remotely sensed data such as high correlation high redundancy etc. are investigated. Based on the above analysis we propose to use the principal component analysis to capture the main information existing in the hyperspectral images and reduce its dimensionality consequently. Next we use neural networks to classify the reduced hyperspectral data. Since the back-propagation neural network we used is easy to suffer from the local minimum problem we adopt a genetic algorithm to optimize the BP network's weights and the threshold. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.
	Comparison between sliding spectral method and back propagation method for radio occultation data 期刊论文 OAI收割 ACTA PHYSICA SINICA, 2011, 卷号: 60, 期号: 9, 页码: 1 作者: Xu Xian-Sheng; Guo Peng; Huang Si-Xun; Xiang Jie 收藏 \| 浏览/下载：19/0 \| 提交时间：2015/08/19 GPS/LEO radio occultation multiple-phase-screen model back propagation method sliding spectral method
	Back propagation method for GPS radio occultation data 期刊论文 OAI收割 CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2011, 卷号: 54, 期号: 9, 页码: 2193-2200 作者: Xu Xian-Sheng; Huang Si-Xun; Guo Peng; Hong Zhen-Jie 收藏 \| 浏览/下载：31/0 \| 提交时间：2015/08/19 GPS/LEO radio occultation Atmospheric multipath Back propagation method Geometric optics method Multiple-phase-screen model
	Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms 期刊论文 OAI收割 FRONTIERS OF COMPUTER SCIENCE IN CHINA, 2009, 卷号: 3, 期号: 2, 页码: 167-176 作者: Yu, Lean; Wang, Shouyang; Lai, Kin Keung \| 收藏 \| 浏览/下载：15/0 \| 提交时间：2018/07/30 back-propagation neural network adaptive smoothing momentum heuristic method foreign exchange rates forecasting
	BP neural network application on surface temperature measurement system based on colorimetry (EI CONFERENCE) 会议论文 OAI收割 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, July 8, 2007 - July 12, 2007, Chengdu, China 作者: Sun Z.-Y. 收藏 \| 浏览/下载：21/0 \| 提交时间：2013/03/25 Measurement of the features of infrared radiation is very important for the precaution and discrimination of missiles and relevant research is worthy in military application. The measurement of target's surface temperature is the foundation of infrared radiation characteristics measurement. The principle and configuration of target's surface temperature measurement system based on colorimetry is introduced the measurement model is deduced and the processes of temperature measurement are presented. Least-square method and back-propagation neural network method are both used to deal with the demarcating data. Compared with the least-square method Back-propagation neural network has more advantages such as high precision good applicability and so on.
	Intelligent MRTD testing for thermal imaging system using ANN (EI CONFERENCE) 会议论文 OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China Sun J.; Ma D. 收藏 \| 浏览/下载：27/0 \| 提交时间：2013/03/25 The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly we use frame grabber to capture the 4-bar target image data. Then according to image gray scale we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets along with known target visibility are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.