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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Al-Duwaish, H.N., Ali, S.S.A. (2001). Hammerstein Model Identification Using Radial Basis Functions Neural Networks. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_131
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DOI: https://doi.org/10.1007/3-540-44668-0_131
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