Weighted Probabilistic Neural Network Based on the Sensitivity Analysis

Gaodeng Guo, Fangyi Wan, Xingliang Yu

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
编辑Ping Ding, Chuan Li, Shuai Yang, Ping Ding, Rene-Vinicio Sanchez
出版商Institute of Electrical and Electronics Engineers Inc.
1049-1054
页数6
ISBN(电子版)9781538653791
DOI
出版状态已出版 - 4 1月 2019
活动2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018 - Chongqing, 中国
期限: 26 10月 201828 10月 2018

出版系列

姓名Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018

会议

会议2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
国家/地区中国
Chongqing
时期26/10/1828/10/18

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