Pattern recognition of gases of petroleum based on RBF model

被引：2

作者：

Barbosa, MSS ^{[1
]}

Ludermir, TB ^{[1
]}

Santos, MS ^{[1
]}

dos Santos, FL ^{[1
]}

de Souza, JEG ^{[1
]}

de Melo, CP ^{[1
]}

机构：

[1] UESB, BR-45100000 Vitoria Da Conquista, BA, Brazil

来源：

VII BRAZILIAN SYMPOSIUM ON NEURAL NETWORKS, PROCEEDINGS | 2002年

关键词：

Accidents; Atmosphere; Atmospheric modeling; Electronic noses; Gases; Instruments; Neural networks; Pattern analysis; Pattern recognition; Petroleum;

D O I：

10.1109/SBRN.2002.1181445

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the case of aerial accident spreading out dangerous gases into the atmosphere, an instrument called electronic nose can warn about the beginning of petroleum derived leaks. In this paper we present the architecture of the neural network for pattern recognition of gases of petroleum based on an RBF model. With this model we analyzed the pattern recognition of five gases: ethane, methane, propane, butane and carbon monoxide, separated in three classes of problems. © 2002 IEEE.

引用

页码：111 / 111

页数：1

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