Identification of Hammerstein models using multivariate statistical tools

The iterative Narendra-Gallman algorithm (NGA) for the identification of a nonlinear system representable by the Hammerstein structure is extended to perform simultaneous structure determination and parameter estimation of multivariable chemical process systems. The parameters of the linear system obtained in state space form using canonical correlations analysis and the coefficients of the polynomial type nonlinear elements are alternately adjusted, until convergence, to obtain the model. The theory is illustrated using data from an experimental heat exchanger and the simulation example of a realistically complex acid-base neutralization tank.

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