Classification of Area Flowing Based on KRNN Method

2011 ◽  
Vol 255-260 ◽  
pp. 2855-2859
Author(s):  
Xiang Sun
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Kernel Principal Component Analysis ◽  
Analysis Method ◽  
Graduate Employment ◽  
Principal Component Analysis Method ◽  
The Neural Network

It is hard to search the influence variables and to classify the flowing areas of graduate employment due to the complex factor inputs. Recently the neural network method has been successfully employed to solve the problem. However the classification result is not ideal due to the nonlinearity and noise. In this work, by combining Recurrent Neural Network (RNN) with Kernel Principal Component Analysis (KPCA), a KRNN model is presented, based on which, the flowing areas of graduate employment is tried to be classified, and the complex factor problem has been well dealt with. In the model, RNN with Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA) as the feature extraction is introduced in as comparison. And then by an empirical study with actual data, it is shown that the proposed methods can both achieve good classification performance comparing with NN method. And the Kernel Principal Component Analysis method performs better than the Principal Component Analysis method.

scholarly journals Comprehensive evaluation of robotic global performance based on modified principal component analysis

2020 ◽  
Vol 17 (4) ◽  
pp. 172988141989688
Author(s):  
Liming Li ◽  
Jing Zhao ◽  
Chunrong Wang ◽  
Chaojie Yan
Keyword(s):  
Principal Component Analysis ◽  
Dimension Reduction ◽  
Kernel Function ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Polynomial Kernel ◽  
Analysis Method ◽  
Principal Component Analysis Method ◽  
Global Performance

The multivariate statistical method such as principal component analysis based on linear dimension reduction and kernel principal component analysis based on nonlinear dimension reduction as the modified principal component analysis method are commonly used. Because of the diversity and correlation of robotic global performance indexes, the two multivariate statistical methods principal component analysis and kernel principal component analysis methods can be used, respectively, to comprehensively evaluate the global performance of PUMA560 robot with different dimensions. When using the kernel principal component analysis method, the kernel function and parameters directly have an effect on the result of comprehensive performance evaluation. Because kernel principal component analysis with polynomial kernel function is time-consuming and inefficient, a new kernel function based on similarity degree is proposed for the big sample data. The new kernel function is proved according to Mercer’s theorem. By comparing different dimension reduction effects of principal component analysis method, the kernel principal component analysis method with polynomial kernel function, and the kernel principal component analysis method with the new kernel function, the kernel principal component analysis method with the new kernel function could deal more effectively with the nonlinear relationship among indexes, and its calculation result is more reasonable for containing more comprehensive information. The simulation shows that the kernel principal component analysis method with the new kernel function has the advantage of low time consuming, good real-time performance, and good ability of generalization.

SMARTPHONE-BASED DIAGNOSTIC MODEL FOR HYPERTENSION USING FEATURES FROM PHOTOPLETHYSMOGRAM

2020 ◽  
Vol 32 (04) ◽  
pp. 2050027
Author(s):  
V. Devaki ◽  
T. Jayanthi
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Pulse Rate ◽  
Selection Process ◽  
Principal Component ◽  
Component Analysis ◽  
Band Pass Filter ◽  
Analysis Method ◽  
Principal Component Analysis Method

Photoplethysmography (PPG) is an optical technique which measures blood volume changes in the arterial blood using red and IR LEDs of wavelengths 660[Formula: see text]nm and 940[Formula: see text]nm, respectively. This paper proposes a methodology to measure the pulse rate from the video signal obtained using an LETV-LE MAX 2 mobile phone’s camera and also to evaluate hypertension. The Android smartphone records the intensity of light reflected from the index finger. The recorded video is separated into red, green and blue frames. Since the red video frames returned useful plethysmographic information, they are filtered using Butterworth band-pass filter and power spectral density analysis was performed on them. The immediate peak gives the pulse rate of the respective subject. Fifteen features of pulse waveform are extracted and by performing the feature selection process, seven features are selected and they undergo classification process using a neural network. The feature selection process is performed by using the eigenvalues of the principal component analysis method. The eigenvalues obtained from this method show the degree of variation present in the data. The eigenvalue that is near or close to zero gives the principal components. The features that are selected by the feature selection process of principal component analysis method are peak interval, settling time, rise time, normalized PPG, peak-to-peak amplitude, first derivative and second derivative. While performing the classification process using a neural network, the accuracy of prediction was calculated for both the normal and hypertensive subjects.

scholarly journals Application of Principal Component Analysis Method to Determine Prediction Indexes Sensibility Sequence

2011 ◽  
Vol 26 ◽  
pp. 1346-1351
Author(s):  
Yang Guo-liang ◽  
Wang Can-zhao ◽  
Wu Shi-yue ◽  
Jia Li-qing ◽  
Zhang Sheng-zhu
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Principal Component Analysis Method
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