Design of a Gas Detection System Based on BP Neural Network

2011 ◽  
Vol 55-57 ◽  
pp. 1819-1823
Author(s):  
Yin Long Wang ◽  
Ke Cheng Lin ◽  
Xi Wu Wang ◽  
Zhi Guang Geng ◽  
Qi Gen Zhong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Sensor ◽  
Sensor Array ◽  
Detection System ◽  
Gas Detection ◽  
Gas Sensor Array ◽  
Array Technology ◽  
Artificial Neural ◽  
Different Gases

On the basis of the brief overview of principles of the gas detection system, this paper has analyzed the characteristics, structure and identification theory to explain the method of gas detection based on an artificial neural network. And it has analyzed and researched gas detection system based on neural network and thus solved the problems such as cross-sensitiveness in present gas sensor. The results show that the gas sensor array "cross-sensitive" issue can be effectively solved through the combination of the pattern recognition of artificial neural network and the gas sensor array technology, which accordingly realizes qualitative identification for different gases and has broad application prospects.

A Zero-Shot Learning Method Using Artificial Neural Network for Drift Calibration of Gas Sensor Array

2020 ◽  
Vol MA2020-01 (26) ◽  
pp. 1856-1856
Author(s):  
Yu-Chieh Cheng ◽  
Ting-I Chou ◽  
Jye-Luen Lee ◽  
Shih-Wen Chiu ◽  
Kea Tiong Tang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Sensor ◽  
Sensor Array ◽  
Learning Method ◽  
Gas Sensor Array ◽  
Artificial Neural

Portable electronic nose system with gas sensor array and artificial neural network

2000 ◽  
Vol 66 (1-3) ◽  
pp. 49-52 ◽  
Author(s):  
Hyung-Ki Hong ◽  
Chul Han Kwon ◽  
Seung-Ryeol Kim ◽  
Dong Hyun Yun ◽  
Kyuchung Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Sensor Array ◽  
Gas Sensor Array ◽  
Artificial Neural ◽  
Electronic Nose System

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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