The Study of Power Customer Classification Based on Principal Component Analysis and Improved Back Propagation Neural Network

2009 ◽  
Author(s):  
Jingmin Wang ◽  
Chunye Wang ◽  
Zhenjia Wang
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Customer Classification

Detection of Citrus HuangLongBing Based on Principal Component Analysis and Back Propagation Neural Network

2014 ◽  
Vol 43 (4) ◽  
pp. 430002 ◽  
Author(s):  
邓小玲 DENG Xiao-ling ◽  
孔晨 KONG Chen ◽  
吴伟斌 WU Wei-bin ◽  
梅慧兰 MEI Hui-lan ◽  
李震 LI Zhen ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Citrus Huanglongbing

scholarly journals Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1379 ◽  
Author(s):  
Zhuang Yang ◽  
Qu Zhou ◽  
Xiaodong Wu ◽  
Zhongyong Zhao ◽  
Chao Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Water Content ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Transformer Oil ◽  
Bpnn Model

The water content in oil is closely related to the deterioration performance of an insulation system, and accurate prediction of water content in oil is important for the stability and security level of power systems. A novel method of measuring water content in transformer oil using multi frequency ultrasonic with a back propagation neural network that was optimized by principal component analysis and genetic algorithm (PCA-GA-BPNN), is reported in this paper. 160 oil samples of different water content were investigated using the multi frequency ultrasonic detection technology. Then the multi frequency ultrasonic data were preprocessed using principal component analysis (PCA), which was implemented to obtain main principal components containing 95% of original information. After that, a genetic algorithm (GA) was incorporated to optimize the parameters for a back propagation neural network (BPNN), including the weight and threshold. Finally, the BPNN model with the optimized parameters was trained with a random 150 sets of pretreatment data, and the generalization ability of the model was tested with the remaining 10 sets. The mean squared error of the test sets was 8.65 × 10−5, with a correlation coefficient of 0.98. Results show that the developed PCA-GA-BPNN model is robust and enables accurate prediction of a water content in transformer oil using multi frequency ultrasonic technology.

scholarly journals Research on prediction model of thermal and moisture comfort of underwear based on principal component analysis and Genetic Algorithm–Back Propagation neural network

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Pengpeng Cheng ◽  
Daoling Chen ◽  
Jianping Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network

AbstractIn order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

scholarly journals Face Recognition Based on PCA Algorithm

2013 ◽  
pp. 79-83
Author(s):  
Taranpreet Singh Ruprah
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Histogram Equalization ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
The Face

This paper is proposed the face recognition method using PCA with neural network back error propagation learning algorithm .In this paper a feature is extracted using principal component analysis and then classification by creation of back propagation neural network. We run our algorithm for face recognition application using principal component analysis, neural network and also calculate its performance by using the photometric normalization technique: Histogram Equalization and comparing with Euclidean Distance, and Normalized correlation classifiers. The system produces promising results for face verification and face recognition. Demonstrate the recognition accuracy for given number of input pattern.

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