TY - GEN
T1 - Weighted Probabilistic Neural Network Based on the Sensitivity Analysis
AU - Guo, Gaodeng
AU - Wan, Fangyi
AU - Yu, Xingliang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/4
Y1 - 2019/1/4
N2 - Due to low training complexity, high stability, quick convergence and simple construction, the probabilistic neural network (PNN) has got extensive application in many fields. Because of the lack of additional weighting factors inside PNN model structure, the PNN using the Sensitivity Analysis (SA) has been improved in this paper. The weight coefficients and compensating factors are introduced into the network and put between pattern layer and summation layer to create the weighted probabilistic neural network (WPNN). The weights are derived using the sensitivity analysis procedure when the radial kernels are used as the output of the pattern layer. At the same time, compensating factors compensate the impact of the SA among the patterns. The performance of the WPNN is examined in contradistinctive experiments. Meanwhile, WPNN is used in fault diagnosis of the aircraft wing skin to prove feasibility of WPNN. The results show that the WPNN is feasible and has better performance in prediction accuracy.
AB - Due to low training complexity, high stability, quick convergence and simple construction, the probabilistic neural network (PNN) has got extensive application in many fields. Because of the lack of additional weighting factors inside PNN model structure, the PNN using the Sensitivity Analysis (SA) has been improved in this paper. The weight coefficients and compensating factors are introduced into the network and put between pattern layer and summation layer to create the weighted probabilistic neural network (WPNN). The weights are derived using the sensitivity analysis procedure when the radial kernels are used as the output of the pattern layer. At the same time, compensating factors compensate the impact of the SA among the patterns. The performance of the WPNN is examined in contradistinctive experiments. Meanwhile, WPNN is used in fault diagnosis of the aircraft wing skin to prove feasibility of WPNN. The results show that the WPNN is feasible and has better performance in prediction accuracy.
KW - Compensating factors
KW - Sensitivity analysis
KW - Weighted coefficients
KW - Weighted probabilistic neural network
UR - http://www.scopus.com/inward/record.url?scp=85061793513&partnerID=8YFLogxK
U2 - 10.1109/PHM-Chongqing.2018.00186
DO - 10.1109/PHM-Chongqing.2018.00186
M3 - 会议稿件
AN - SCOPUS:85061793513
T3 - Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
SP - 1049
EP - 1054
BT - Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
A2 - Ding, Ping
A2 - Li, Chuan
A2 - Yang, Shuai
A2 - Ding, Ping
A2 - Sanchez, Rene-Vinicio
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
Y2 - 26 October 2018 through 28 October 2018
ER -