Abstract
The goal of the local linear model trees (LOLIMOT) toolbox for MATLAB® is to provide the industrial user and the research engineer with a fast and easy-to-use software package for nonlinear system identification. The emphasis is on shortening the overall modeling development time by reducing the number of required trial-and-error steps for identification, rather than on variability. In order to achieve this goal, the implemented algorithm has to converge in a reliable manner without any random influences (as they may be caused by random initializations for nonlinear optimization techniques). Furthermore, an appropriate problem dependent model complexity should be suggested to the user in order to avoid the requirement for tedious tuning. No (at least no non-interpretable) "fiddle" parameters should exist on which the identification results depend in a sensitive manner. The core of the LOLIMOT toolbox is a fast incremental construction algorithm for local linear neuro-fuzzy models also known as Takagi-Sugeno fuzzy models.
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