NDLI: Comparison of neural network algorithms based on gas qualitative analysis

Content Provider	IEEE Xplore Digital Library
Author	Yu Mingyan Shi Yunbo Zhao Wenjie Feng Qiaohua Wang Xuan Sun Lining
Copyright Year	2011
Description	Author affiliation: Measurement-control Tech & Communications Engineering College, The higher educational key laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, China (Yu Mingyan; Shi Yunbo; Zhao Wenjie; Feng Qiaohua; Wang Xuan; Sun Lining)
Abstract	For the problem of gas qualitatively identify in the field of gas detection, this paper is based on the multi-sensor and pattern recognition of neural network, the uniform change voltage of the sensor output is simulated by the gradient descent algorithm, the additional momentum algorithm and the LM algorithm of neural network, then compare the three simulation results of the three algorithms, the result proves that the LM algorithm is the optimal algorithm of the data simulation in this paper, in the range of allowable error, completed the gas qualitative identification.
Starting Page	1176
Ending Page	1180
File Size	936737
Page Count	5
File Format	PDF
ISBN	9781457703980
e-ISBN	9781457703997
DOI	10.1109/IFOST.2011.6021230
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Gases Electric potential Voltage measurement BP neural network Simulation gas sensor Surface treatment qualitative identification
Content Type	Text
Resource Type	Article

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