Abstract
A novel algorithm to local model network (LMN) generation is proposed. This algorithm allows to use prior knowledge about the process in many different levels. The Van de Vusse benchmark problem is used as example. This system exhibits a change in gain at the peak of the reactor yield, displays non-minimum phase behavior (inverse response) for operating points on the left of this peak and minimum-phase behavior with overshoot for operating points on the right. To deal with those problems, the space is divided into sub-regions, and for each one a model is identified around its centers. The whole space is described by the combination of the local models with a new weighting function based on the generalized gauss function.
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