TY - GEN
T1 - Comparison of neural network algorithms based on gas qualitative analysis
AU - Yu, Mingyan
AU - Shi, Yunbo
AU - Zhao, Wenjie
AU - Feng, Qiaohua
AU - Wang, Xuan
AU - Sun, Lining
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - BP neural network
KW - gas sensor
KW - qualitative identification
UR - https://www.scopus.com/pages/publications/80053429775
U2 - 10.1109/IFOST.2011.6021230
DO - 10.1109/IFOST.2011.6021230
M3 - 会议稿件
AN - SCOPUS:80053429775
SN - 9781457703966
T3 - Proceedings of the 6th International Forum on Strategic Technology, IFOST 2011
SP - 1176
EP - 1180
BT - Proceedings of the 6th International Forum on Strategic Technology, IFOST 2011
T2 - 6th International Forum on Strategic Technology, IFOST 2011
Y2 - 22 August 2011 through 24 August 2011
ER -