Fuzzy local linearization and local basis function expansion in nonlinear system modeling

Fuzzy local linearization is compared with local basis function expansion f or modeling unknown nonlinear processes. First-order Takagi-Sugeno fuzzy mo del and the analysis of variance (ANOVA) decomposition are combined for the fuzzy local linearization of nonlinear systems, in which B-splines are use d as membership functions of the fuzzy sets for input space partition. A mo dified algorithm for adaptive spline modeling of observation data (MASMOD) is developed for determining the number of necessary B-splines and their kn ot positions to achieve parsimonious models. This paper illustrates that fu zzy local linearization models have several advantages over local basis fun ction expansion based models in nonlinear system modeling.