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

Simple item page
dc.contributor.authorMohammadzaheri, M.
dc.contributor.authorChen, L.
dc.contributor.authorGhaffari, A.
dc.contributor.authorWillison, J.
dc.date.issued2009
dc.description.abstractThis 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. © 2008 Elsevier B.V. All rights reserved.
dc.description.statementofresponsibilityMorteza Mohammadzaheri, Lei Chen, Ali Ghaffari and John Willison
dc.identifier.citationSimulation Modelling Practice and Theory, 2009; 17(2):398-407
dc.identifier.doi10.1016/j.simpat.2008.09.015
dc.identifier.issn1569-190X
dc.identifier.issn1878-1462
dc.identifier.orcidChen, L. [0000-0002-2269-2912]
dc.identifier.orcidWillison, J. [0000-0003-1892-1089]
dc.identifier.urihttp://hdl.handle.net/2440/60438
dc.language.isoen
dc.publisherElsevier Science BV
dc.rightsCopyright © 2008 Elsevier B.V. All rights reserved.
dc.source.urihttps://doi.org/10.1016/j.simpat.2008.09.015
dc.subjectArtificial neural network
dc.subjectDe-superheater
dc.subjectModeling
dc.subjectPower plant
dc.titleA combination of linear and nonlinear activation functions in neural networks for modeling a de-superheater
dc.typeJournal article
pubs.publication-statusPublished

Files

Collections

Mechanical Engineering publications