TY - GEN
T1 - Application of optimal IA-BP algorithm in moisture content detecting with microwave resonator
AU - Jiang, Yu
AU - Cao, Jun
AU - Yang, Guo Hui
PY - 2005
Y1 - 2005
N2 - The linear regression that founds the function between the moisture content and the detecting parameters brings significant errors which can be reduced by BP algorithm. There is one fatal disadvantage that conventional BP algorithm tends to get into infinitesimal locally, which worsens the stability of the measurement precision. An evolutionary neural network model based on optimal IA-BP algorithm is presented in this paper. In the model, IA algorithm is first used for global search and then BP algorithm for local search. The optimal IA-BP algorithm has the merits of high prediction precision, rapid convergence, global superiority and accuracy for optimization compared with conventional BP algorithm. Experiments shows the remarkable improvements in the measurement with the mean squared error 0.0125, the mean absolute error 0.0715, the mean relative error 0.1186 and the certain coefficient 0.9965 between the predicted moisture content and the real value.
AB - The linear regression that founds the function between the moisture content and the detecting parameters brings significant errors which can be reduced by BP algorithm. There is one fatal disadvantage that conventional BP algorithm tends to get into infinitesimal locally, which worsens the stability of the measurement precision. An evolutionary neural network model based on optimal IA-BP algorithm is presented in this paper. In the model, IA algorithm is first used for global search and then BP algorithm for local search. The optimal IA-BP algorithm has the merits of high prediction precision, rapid convergence, global superiority and accuracy for optimization compared with conventional BP algorithm. Experiments shows the remarkable improvements in the measurement with the mean squared error 0.0125, the mean absolute error 0.0715, the mean relative error 0.1186 and the certain coefficient 0.9965 between the predicted moisture content and the real value.
KW - Evolutionary neural network
KW - Moisture content measurement
KW - Open resonant microwave moisture sensors
KW - Optimal IA-BP algorithm
UR - https://www.scopus.com/pages/publications/33749054746
M3 - 会议稿件
AN - SCOPUS:33749054746
SN - 078039030X
SN - 9780780390300
T3 - 2005 IEEE International Workshop on Intelligent Signal Processing - Proceedings
SP - 155
EP - 158
BT - 2005 IEEE International Workshop on Intelligent Signal Processing - Proceedings
T2 - 2005 IEEE International Workshop on Intelligent Signal Processing
Y2 - 1 September 2005 through 3 September 2005
ER -