A combination of linear and nonlinear activation functions in neural networks for modeling a de-superheater |
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Authors: | Morteza Mohammadzaheri Lei Chen Ali Ghaffari John Willison |
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Affiliation: | 1. Mechanical Engineering School, University of Adelaide, Australia;2. Mechanical Engineering Faculty, K.N.Toosi University of Technology, Iran;3. Center for Learning and Professional Development, University of Adelaide, Australia;1. Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China;2. Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People''s Hospital, Shanghai 200233, China;1. Mecánicas Bolea S.A., Aeronautics Division, Avda. Bruselas s/n, Cartagena 30353, Spain;2. Dpto. de Ing. de Mat. y Fabricación, University Politécnica de Cartagena, C/ Doctor Flemming s/n, Cartagena 30202, Spain;3. Dpto. de Ing. Mecánica, University de Navarra, Paseo de Manuel Lardizabal 13, Donostia 20018, Spain |
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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. |
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