Extended model set, global data and threshold model identification of severely non-linear systems

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Abstract
New parameter estimation algorithms, based on an extended model set, a global data model and a threshold model formulation, are derived for identifying severely non-linear systems. It is shown that in each case an integrated structure determination and parameter estimation algorithm based on an orthogonal decomposition of the regression matrix can be derived to provide procedures for identifying parsimonious models of unknown systems with complex structure. Simulation studies are included to illustrate the techniques discussed.