Abstract
A control-oriented multivariable Hammerstein model is used to identify the strongly nonlinear dynamics of fuel cell systems that are described by nonlinear differential or differential-algebraic equations. Within the Hammerstein model framework, the static nonlinear part is constructed by a wavelet network, and the linear dynamic part is described by a discrete-time transfer function of the state-space model. For prescribed input-output patterns for high-order fuel cell systems, simulations demonstrate the accuracy of system identification using wavelet networks in the Hammerstein structure that is better than that in the neural network structure.
Original language | English |
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Pages (from-to) | 625-630 |
Number of pages | 6 |
Journal | JOURNAL of CHEMICAL ENGINEERING of JAPAN |
Volume | 46 |
Issue number | 9 |
DOIs | |
Publication status | Published - 2013 Sep 25 |
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)
- Chemistry(all)