Gradient Approach to Curve Fitting with Incomplete Data
The paper deals with the problem of nonlinear curve fitting in situation of incomplete data. Research was motivated by the industrial identification of Hammerstein models. It was noticed that for the model robustness and quality the fitness of the static nonlinear element is much more crucial then efficiency of dynamic operation of linear part. Industrial data are incomplete, i.e. they do not cover the whole process domain. It is due to the operation around selected steady states, close loop operation, extensive manual model use, etc. In case of multi-regional approach we often get regions with no data. Proposed methodology is addressing that issue. Included results are for both simulation and real industrial case.