Nonlinear dynamic system modelling in noisy environment using multiple model approach

A nonlinear dynamic process can be described as a composition of several local affine models selected according to the process operating conditions. Such a compound system requires the identification of the local models from data. This work addresses a method for the identification and the optimal selection of the local affine models from a sequence of noisy measurements acquired from the process. The developed technique is applied to the identification of a simulated model.