S. Jakubek, C. Hametner, N. Keuth:
"Total least squares in fuzzy system identification: An application to an industrial engine";
Engineering Applications of Artificial Intelligence,
Takagi-Sugeno fuzzy models have proved to be a powerful tool for the identification of nonlinear
dynamic systems. Their generic nonlinear model representation is particularly useful if information
about the structure of the nonlinearity is available. In view of a practical applicability in industrial
applications two important issues are addressed. First, the problem of unbiased estimation of local
model parameters in the presence of input and output noise is considered. For that purpose the concept
of total least squares for parameter estimation is reviewed and a related partitioning algorithm based on
statistical criteria is presented. Second, the steady-state accuracy of dynamic models is addressed.
A concept of constrained TLS parameter optimisation is introduced which enforces the adherence of the
model to selected steady-state operating points and thus significantly improves the model accuracy
during steady-state phases. Results from a simulation model and from an industrial gas engine power
plant demonstrate the capabilities of the proposed concepts.
Takagi-Sugeno fuzzy models, Nonlinear system identification, Identification algorithms, Total least squares, Gas engine
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