Abstract
A new method for the identification of the nonlinear Hammerstein Model consisting a static nonlinearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modeled by radial basis function neural networks (RBFNN) and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the RBFNN and the parameters of the ARMA model.
Cite
CITATION STYLE
Al-Duwaish, H. N., & Ali, S. S. A. (2001). Hammerstein model identification using radial basis functions neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2130, pp. 951–956). Springer Verlag. https://doi.org/10.1007/3-540-44668-0_131
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