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长春光学精密机械与物... [3]
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会议论文 [3]
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2011 [1]
2009 [1]
2007 [1]
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专题:长春光学精密机械与物理研究所
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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.
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浏览/下载:28/0
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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.
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.
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浏览/下载:22/0
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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.
BP neural network application on surface temperature measurement system based on colorimetry (EI CONFERENCE)
会议论文
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
Sun Z.-Y.
;
Cai S.
;
Qiao Y.-F.
;
Zhu W.
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浏览/下载:15/0
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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.
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