Computer Science ›› 2015, Vol. 42 ›› Issue (12): 302-306.

Previous Articles     Next Articles

Mixed Application of GHA Based on PCA in BP Neural Network

FAN Yan, WU Xiao-jun, SHAO Chang-bin and SONG Xiao-ning   

  • Online:2018-11-14 Published:2018-11-14

Abstract: In view of the defects resulting from the combination of traditional method of feature extraction and BP neural network,this paper presented a new classification model “PCABP network”.Firstly,the PCA eigenvector is used to initialize the initial-layer weight matrix of the PCABP network,thus,the initial-layer of the new classification model “PCABP Network” replaces the role of PCA in the function of feature extraction.Secondly,in the training process,with the application of GHA and GD algorithm,dynamic adjustment to the projection weights matrix of the initial layer has been achieved,and accordingly,the PCA eigenvector has been optimized.This method optimizes “category separation” and “feature extraction” from source samples,finds out the best connection point between sample dimension reduction and classification,and replaces the traditional recognition pattern “firstly separate feature extraction,then classification by the use of classifier”.The experiment based on FERET face library verifies the effectiveness of this method.

Key words: BP beural betwork,PCA,GHA,RPROP,Gender classification,Mixed neural network

[1] 边肇祺,张学工.模式识别(第二版)[M].北京:清华大学出版社,1999 Bian Zhao-qi,Zhang Xue-gong.Pattern Recognition (Second Edition) [M].Beijing:Tsinghua University Press,1999
[2] 王成,郭飞,赖雄鸣,等.典型代数统计的人脸特征提取融合[J].小型微型计算机系统,2014,5(7):1662-1666 Wang Cheng,Guo Fei,Lai Xiong-ming,et al.Face Feature Extraction Fusion Based on Classical Algebraic Statistics[J].Mini-Micro Systems,2014,5(7):1662-1666
[3] 方晶晶,李振波,姜宇.人体肤色区域的自适应模型分割方法[J].计算机辅助设计与图形学学报,2013,5(2):229-234 Fang Jing-jing,Li Zhen-bo,Jiang Yu.Human Skin Color Region Segmentation Based on Adaptive Model[J].Journal of Compu-ter-Aided Design & Compu-ter Graphics,2013,5(2):229-234
[4] Hinton G E,Salakhutdinov R R.Reducing the dimensionality of data with neural networks.[J].Science(New York,N.Y.),2006,313(5786):504-507
[5] 余凯,贾磊,陈雨强,等.深度学习的昨天、今天和明天[J].计算机研究与发展,2013,50(9):1799-1804 Yu Kai,Jia Lei,Chen Yu-qiang,et al.Deep Learning:Yesterday,Today,and Tomorrow[J].Journal of Computer Research and Development,2013,50(9):1799-1804
[6] 聂仁灿,姚绍文,周冬明.基于简化脉冲耦合神经网络的人脸识别[J].计算机科学,2014,1(2):297-301 Nie Ren-can,Yao Shao-wen,Zhou Dong-ming.Face Recognition Using Simplified Pulse Coupled Neural Network[J].Computer Science,2014,1(2):297-301.
[7] 刘贵松,王晓彬.采用自适应GHA神经网络的分类器设计[J].电子科技大学学报,2007,6(6):1241-1244 Liu Gui-song,Wang Xiao-bin.Classifier Design Using Adaptive GHA Neural Networks[J].Journal of University of Electornic Science and Technology of China,2007,6(6):1241-1244
[8] 杨存祥,朱堔,解豪杰.基于RPROP神经网络算法的异步电动机故障诊断[J].电力自动化设备,2012,2(1):80-83 Yang Cun-xiang,Zhu Chen,Xie Hao-jie.Fault Diagnosis Based on RPROP Neural Network for Asynchronous Motor[J].Electric Power Automation Equipment,2012,2(1):80-83
[9] Liu X,Chen T,Kvijava Kumar B V.Face authentication formultiple subjects using eigenflow [J].Pattern Recognition,2003,36(2):313-328
[10] Sander T D.Optimal unsupervised learning in a single-layer li-near feed forward neural network [J].Neural Networks,1989,2:459-473
[11] Alex L,Sandor C,Der Nasser Z,et al.Dual-band passive infrared imagery for automatic clutter rejection [M].ICIP,2000
[12] Belhumeur P N,Hespanha J P,Kriengman D J.Eigenfaces vs.Fisherfaces:Recognition Using Class Specific Linear Projection [J].IEEE Trans.Pattern Analysis and Machine Intelligence,1997,9(7):711-720

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105, 130 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] LU Jia-wei, MA Jun, ZHANG Yuan-ming and XIAO Gang. Service Clustering Approach for Global Social Service Network[J]. Computer Science, 2018, 45(3): 204 -212 .
[10] ZHU Shu-qin, WANG Wen-hong and LI Jun-qing. Chosen Plaintext Attack on Chaotic Image Encryption Algorithm Based on Perceptron Model[J]. Computer Science, 2018, 45(4): 178 -181, 189 .