2005 IEEE Engineering in Medicine and Biology 27th Annual Conference
DOI: 10.1109/iembs.2005.1615759
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The goal of this paper is to provide a novel procedure for the identification of nonlinear models which exhibit a quadratic dependence on the state variables. These models turn out to be very useful for the description of a large class of biochemical processes with particular reference to the genetic networks regulating the cell cycle. The proposed approach is validated through extensive computer simulations on randomly generated systems.