Abstract
This chapter describes a method for the identification of a SISO and MIMO Hammerstein systems based on Least Squares Support Vector Machines (LS-SVMs). The aim of this chapter is to give a practical account of the works [14] and [15], adding to this material new insights published since. The identification method presented in this chapter gives estimates for the parameters governing the linear dynamic block represented as an ARX model, as well as for the unknown static nonlinear function.
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Goethals, I., Pelckmans, K., Falck, T., Suykens, J.A.K., De Moor, B. (2010). NARX Identification of Hammerstein Systems Using Least-Squares Support Vector Machines. In: Giri, F., Bai, EW. (eds) Block-oriented Nonlinear System Identification. Lecture Notes in Control and Information Sciences, vol 404. Springer, London. https://doi.org/10.1007/978-1-84996-513-2_15
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