A combination of linear and nonlinear activation functions in neural networks for modeling a de-superheater

Morteza Mohammadzaheri, Lei Chen, Ali Ghaffari, John Willison

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

This paper deals with modeling a power plant component with mild nonlinear characteristics using a modified neural network structure. The hidden layer of the proposed neural network has a combination of neurons with linear and nonlinear activation functions. This approach is particularly suitable for nonlinear system with a low grade of nonlinearity, which can not be modeled satisfactorily by neural networks with purely nonlinear hidden layers or by the method of least square of errors (the ideal modeling method of linear systems). In this approach, two channels are installed in a hidden layer of the neural network to cover both linear and nonlinear behavior of systems. If the nonlinear characteristics of the system (i.e. de-superheater) are not negligible, then the nonlinear channel of the neural network is activated; that is, after training, the connections in nonlinear channel get considerable weights. The approach was applied to a de-superheater of a 325 MW power generating plant. The actual plant response, obtained from field experiments, is compared with the response of the proposed model and the responses of linear and neuro-fuzzy models as well as a neural network with purely nonlinear hidden layer. A better accuracy is observed using the proposed approach.

Original languageEnglish
Pages (from-to)398-407
Number of pages10
JournalSimulation Modelling Practice and Theory
Volume17
Issue number2
DOIs
Publication statusPublished - Feb 2009

Keywords

  • Artificial neural network
  • De-superheater
  • Modeling
  • Power plant

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Hardware and Architecture

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