Weighted Probabilistic Neural Network Based on the Sensitivity Analysis

Gaodeng Guo, Fangyi Wan, Xingliang Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
EditorsPing Ding, Chuan Li, Shuai Yang, Ping Ding, Rene-Vinicio Sanchez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1049-1054
Number of pages6
ISBN (Electronic)9781538653791
DOIs
StatePublished - 4 Jan 2019
Event2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018 - Chongqing, China
Duration: 26 Oct 201828 Oct 2018

Publication series

NameProceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018

Conference

Conference2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018
Country/TerritoryChina
CityChongqing
Period26/10/1828/10/18

Keywords

  • Compensating factors
  • Sensitivity analysis
  • Weighted coefficients
  • Weighted probabilistic neural network

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