Recognition of spikes of Schizonepeta tenuifolia from different area based on backpropagation neural network coupled with dimension reduction of principal component analysis

2010 ◽  
Vol 35 (14) ◽  
Author(s):  
YAO Weifeng
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Dimension Reduction ◽  
Principal Component ◽  
Component Analysis ◽  
Backpropagation Neural Network ◽  
Schizonepeta Tenuifolia

Dimension Reduction by Local Principal Component Analysis

1997 ◽  
Vol 9 (7) ◽  
pp. 1493-1516 ◽  
Author(s):  
Nandakishore Kambhatla ◽  
Todd K. Leen
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Dimension Reduction ◽  
Image Data ◽  
Principal Component ◽  
Component Analysis ◽  
Significant Loss ◽  
Nonlinear Pca ◽  
Local Linear ◽  
Nonlinear Extensions

Reducing or eliminating statistical redundancy between the components of high-dimensional vector data enables a lower-dimensional representation without significant loss of information. Recognizing the limitations of principal component analysis (PCA), researchers in the statistics and neural network communities have developed nonlinear extensions of PCA. This article develops a local linear approach to dimension reduction that provides accurate representations and is fast to compute. We exercise the algorithms on speech and image data, and compare performance with PCA and with neural network implementations of nonlinear PCA. We find that both nonlinear techniques can provide more accurate representations than PCA and show that the local linear techniques outperform neural network implementations.

scholarly journals Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hongling Hua ◽  
Xiaohui Xie ◽  
Jinjin Sun ◽  
Ge Qin ◽  
Caiyan Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Chemical Sensors ◽  
Chemical Sensor ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis ◽  
Backpropagation Neural Network ◽  
Graphene Foam ◽  
New Strategy

A kind of graphene foam chemical sensor (GFCS) system based on the principal component analysis (PCA) and backpropagation neural network (BPNN) was presented in this paper. Compared with conventional chemical sensors, the GFCS could discriminate various chemical molecules with selectivity without surface modification. The GFCS system consisted of an unmodified graphene foam chemical sensor, an electrical resistance time domain detection system (ERTDS), and a pattern recognition module. The GFCS has been validated via several chemical molecules discrimination including chloroform, acetone, and ether. The experimental results showed that the discrimination accuracy for each molecule exceeded 97% and a single measurement can be achieved in ten minutes. This work may have presented a new strategy for research and application for graphene chemical sensors.

Sign in / Sign up

Export Citation Format

Share Document