Aeroengine exhaust gas temperature prediction using process neural network with time-varying threshold functions

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

Abstract

To predict the aeroengine exhaust gas temperature (EGT) more precisely, a process neuron with time-varying threshold function is proposed in this paper, and then the time-varying threshold process neural network model comprised of the presented process neurons is used for EGT prediction. By introducing a group of appropriate orthogonal basis functions, the input functions, the weight functions and the threshold functions of the time-varying threshold process neural network can be expanded as linear combinations of the given orthogonal basis functions, thus to eliminate the integration operation, then to simplify the time aggregation operation. The corresponding learning algorithm is also presented, and the effectiveness of the time-varying threshold process neural network model is evaluated through the prediction of EGT series from practical aeroengine condition monitoring.

Original languageEnglish
Title of host publicationApplied Materials and Technologies for Modern Manufacturing
Pages2341-2346
Number of pages6
DOIs
StatePublished - 2013
Externally publishedYes
Event3rd International Conference on Applied Mechanics, Materials and Manufacturing, ICAMMM 2013 - Dalian, China
Duration: 24 Aug 201325 Aug 2013

Publication series

NameApplied Mechanics and Materials
Volume423-426
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Conference

Conference3rd International Conference on Applied Mechanics, Materials and Manufacturing, ICAMMM 2013
Country/TerritoryChina
CityDalian
Period24/08/1325/08/13

Keywords

  • Exhaust gas temperature prediction
  • Learning algorithm
  • Orthogonal basis function
  • Time-varying threshold function
  • Time-varying threshold process neural network

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