An Algorithm for Learning Principal Curves with Principal Component Analysis and Back-Propagation Network

2007 ◽  
Author(s):  
Y. H. Wang ◽  
Y. Guo ◽  
Y. C. Fu ◽  
Z. Y. Shen
Keyword(s):  
Principal Component Analysis ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Principal Curves ◽  
Propagation Network

Prediction of hourly indoor Carbon Monoxide concentrations in semiarid regions using Regression and feedforward backpropagation as a hybrid model

2018 ◽  
Vol 5 (1) ◽  
pp. 717-739
Keyword(s):  
Principal Component Analysis ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Natural Ventilation ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Co Concentration

Abstract Accurate site-specific forecasting of indoor hourly carbon monoxide (CO) concentrations in school microenvironments is a key issue in air quality research nowadays due to its impact on children’s health. This paper investigated the improvement prediction of multiple linear regression (MLR) and feed forward back propagation (FFBP) by combining them with principal component analysis (PCA) for predicting indoor CO concentration in Gaza Strip, Palestine. Measurements were carried in 12 schools from October 2012 to May 2013 (one academic year). The results suggested that the selected models are effective forecasting tools and hence can be applicable for short-term forecasting of indoor CO level. The predicted indoor CO concentration values agree strongly well with the measured data with high coefficients of determination (R2) 0.869, 0.870, 0.885 and 0.915 for MLR, PCA-MLR, FFBP and PCA-FFBP, respectively. Overall, results showed that PCA models combined with MLR and PCA with FFBP improved MLR and FFBP models of predicting indoor CO concentration, with reduced errors by as much as 7.14% (PCA-MLR) and 56.6% (PCA-FFBP). Moreover, PCA improved the accuracy of the FFBP model by as much as by 3.3%. Keywords: Natural Ventilation; Children; Indoor Air Quality; Feed forward back propagation; Principal component analysis.

scholarly journals Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Manoj Tripathy
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Power Transformer ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Optimal Number ◽  
Differential Protection ◽  
Harmonic Restraint ◽  
Transformer Differential Protection

This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and overexcitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The proposed algorithm makes use of ratio of voltage to frequency and amplitude of differential current for transformer operating condition detection. This paper presents a comparative study of power transformer differential protection algorithms based on harmonic restraint method, NNPCA, feed forward back propagation neural network (FFBPNN), space vector analysis of the differential signal, and their time characteristic shapes in Park’s plane. The algorithms are compared as to their speed of response, computational burden, and the capability to distinguish between a magnetizing inrush and power transformer internal fault. The mathematical basis for each algorithm is briefly described. All the algorithms are evaluated using simulation performed with PSCAD/EMTDC and MATLAB.

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

Performance Comparison between ANN and PCA Techniques for Road Signs Recognition

2013 ◽  
Vol 393 ◽  
pp. 611-616 ◽  
Author(s):  
Nursabillilah Mohd Ali ◽  
Yasir Mohd Mustafah ◽  
Nahrul Khair Alang Md Rashid
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Performance Comparison ◽  
Feed Forward Neural Network ◽  
Classification Rate ◽  
The Road ◽  
Road Signs

This study reports about a comparison in recognizing road signs between Neural Network and Principal Component Analysis (PCA). The road sign with circular, triangular, octagonal and diamond shapes have been used in this study. Two recognition systems to determine the classes of the road signs class were implemented which are based on Feed Forward Neural Network and Principal Component Analysis (PCA). The performance of the trained classifier using Scaled Conjugate Gradient (SCG) back propagation function in Neural Network and PCA technique were evaluated on our test datasets. The experiments show that the system using PCA has a higher accuracy as compared to Neural Network with a minimum of 94% classification rate of road signs.

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