Embedded system to recognize the heat power of a fuel gas and to classificate the quality of alcohol fuel

Author

Hirayama, Vitor ; Ramirez-Fernandez, Francisco J.

Author_Institution

Unidade Casa Verde, Sao Paulo, Brazil

Volume

2

fYear

2003

fDate

9-11 June 2003

Firstpage

1155

Abstract

This work presents the result obtained to develop an electronic nose to recognize the fuel gas heat power. As a first approach, synthetic data was generated for each sensor. It was considered the use of raw data and the use of a principal component analysis (PCA) to reduce the number of sensors. Two topologies of neural networks have been used, the backpropagation and learning vector quantization (LVQ). A fuzzy inference system (FIS) also has been used as a solution to this problem.

Keywords

backpropagation; embedded systems; fuel; fuzzy set theory; gases; inference mechanisms; neural nets; principal component analysis; vector quantisation; PCA; alcohol fuel quality; backpropagation; electronic nose; embedded system; fuel gas heat power; fuzzy inference system; learning vector quantization; neural networks topologies; principal component analysis; sensor; Backpropagation; Circuit topology; Electronic noses; Embedded system; Fuels; Fuzzy systems; Network topology; Neural networks; Principal component analysis; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, 2003. ISIE '03. 2003 IEEE International Symposium on

Print_ISBN

0-7803-7912-8

Type

conf

DOI

10.1109/ISIE.2003.1267988

Filename

1267988