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	Metal Identification Based on Laser-induced Breakdown Spectroscopy and BP Neural Network 会议论文 OAI收割 International Conference on Electrical Engineering and Automation (ICEEA), Xiamen, China, December 18-19, 2016 作者: Shi YZ(史友振); Sun LX(孙兰香); Zhang, Ying \| 收藏 \| 浏览/下载：36/0 \| 提交时间：2017/09/12 LIBS Scrap metal sorting The BP neural network
	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.
	Design of controlling system in multi-function durability testing device for vehicle vacuum booster with brake master cylinder (EI CONFERENCE) 会议论文 OAI收割 2012 International Conference on Mechanical and Electronic Engineering, ICMEE 2012, June 23, 2012 - June 24, 2012, Hefei, China Hao X.; Zhang R.; Li X.; Wang M. 收藏 \| 浏览/下载：38/0 \| 提交时间：2013/03/25 The quality of vehicle vacuum booster with brake master cylinder is related to the safe of drivers and automobiles. The testing experiment must be executed strictly before leaving factory based on the national standards.The paper introduces the controlling system of multi-function durability testing device which is designed for doing durable testing experiments and Anti-lock braking system (ABS) performance experiments. The structure and theory of device is presented. The controlling system is illuminated in detail. To test the dynamic property this system was identified by a recursive BP neural network. According to the character of a great deal of sensors and actuators the high precision capabilities and reliability the distributed control mode (DCS) including the computer and PLC by RS-485 bus is utilized. The four channels testing experiments are achieved at the same time. The test data is directly memorized into the computer. The results of general endurance and ABS endurance testing experiments are shown to demonstrate the excellent performance of the testing device. 2012 Springer-Verlag Berlin Heidelberg.
	Double inverted pendulum control based on three-loop PID and improved BP neural network (EI CONFERENCE) 会议论文 OAI收割 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, August 5, 2011 - August 7, 2011, Zhangjiajie, Hunan, China 作者: Fan Y. 收藏 \| 浏览/下载：37/0 \| 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.
	A comparison of hardware implementation of BP neural network based on NIOS II single-core and multi-core processor (EI CONFERENCE) 会议论文 OAI收割 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011, April 15, 2011 - April 17, 2011, Wuhan, China 作者: Zhang W.; Zhang H.; Zhang H.; Zhang W. 收藏 \| 浏览/下载：15/0 \| 提交时间：2013/03/25 The hardware implementation of neural network at present is introduced in this paper. And then implementations of neural network based on NIOS II single-core and multi-core processor are compared. At last the results of two methods are given taking BP network fitting sine curve for example. 2011 IEEE.
	Environment modeling of AS-R robot based on BP Neural Network (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Xie M.-J.; Yu X.-L.; Wang Z.-Q. 收藏 \| 浏览/下载：33/0 \| 提交时间：2013/03/25 There are some limitations when environment model of AS-R mobile robot is obtained by only using a separate CCD camera or ultrasonic sensor array. The BP neural network is designed to fuse information of the two sensors to obtain the robot's environment model in this paper. The input of BP neural network is the intercept and slope of the edge line obtained CCD camera and ultrasonic sensor arrays which is processed by through the global coordinates of coordinate transformation. The output of BP network is the fused intercept and slope of the straight edge. Experiment shows that environment model is feasible for the AS-R mobile robot and the environment modeling method has more reliability and accuracy. 2010 IEEE.
	System identification of tracking error and evaluation of tracking performance using BP neural network (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, June 17, 2009 - June 19, 2009, Beijing, China Zhang N.; Shen X.-H. 收藏 \| 浏览/下载：20/0 \| 提交时间：2013/03/25 A novel approach for evaluating the tracking performance of optoelectronic theodolite is proposed. First an equivalent mathematic model of tracking error is established. Then the equivalent sine signal is inputted to the equivalent model and the outputs are sampled. The results of evaluating the tracking performance are obtained based on the statistical calculation of output produced by equivalent model. Equivalent model using the BP (Backprogration) neural network structure is identified. The training method of BP neural network adopts the LM (Levenberg-Marquardt) algorithm for the sake of speeding up training process. The BP neural network is trained and tested by using the training and testing samples gotten from the simulation model of optoelectronic theodolite tracking system under MATLAB/SIMULINK. The estimate errors of equivalent model including average error maximum error and standard error are 2.5872e-0060 2.8 and 1.9. The results show that the equivalent model identified based on BP neural network meets the needs of evaluating the tracking performance of optoelectronic theodolite. The accurate evaluation of tracking performance is achieved. 2009 SPIE.
	A new early stopping algorithm for improving neural network generalization (EI CONFERENCE) 会议论文 OAI收割 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009, October 10, 2009 - October 11, 2009, Changsha, Hunan, China 作者: Liu J.-G.; Wu X.-X. 收藏 \| 浏览/下载：23/0 \| 提交时间：2013/03/25 As generalization ability of neural network was restricted by overfitting problem in the network's training. Early stopping algorithm based on fuzzy clustering was put forward to solve this problem in this paper. Subtractive clustering and Fuzzy C-Means clustering (FCM) were combined to realize optimal division of training set validation set and test set. How to realize this algorithm in backpropagation (BP) network by utilizing neural network toolbox and fuzzy logic toolbox in MATLAB was dwelled on. Early stopping algorithm based on fuzzy clustering and other early stopping algorithms were applied in function approximation and pattern recognition problems in validation experiments. Experiments results indicate that early stopping algorithm based on fuzzy clustering has higher precision in comparison to other early stopping algorithms. Outputs of training set validation set and test set are more accordant. 2009 IEEE.
	An object recognition method based on fuzzy theory and BP networks (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Chuan W.; Ming Z.; Dong Y. 收藏 \| 浏览/下载：19/0 \| 提交时间：2013/03/25 It is difficult to choose eigenvectors when neural network recognizes object. It is possible that the different object eigenvectors is similar or the same object eigenvectors is different under scaling shifting rotation if eigenvectors can not be chosen appropriately. In order to solve this problem the image is edged the membership function is reconstructed and a new threshold segmentation method based on fuzzy theory is proposed to get the binary image. Moment invariant of binary image is extracted and normalized. Some time moment invariant is too small to calculate effectively so logarithm of moment invariant is taken as input eigenvectors of BP network. The experimental results demonstrate that the proposed approach could recognize the object effectively correctly and quickly.