Producing interpretable local models in parametric CMAC by regularization
International Journal of Knowledge-based and Intelligent Engineering Systems
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Original article: T-S fuzzy model-based impulsive control for chaotic systems and its application
Mathematics and Computers in Simulation
A Neuro-Fuzzy Identification of ECG Beats
Journal of Medical Systems
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Fuzzy local linearization is compared with local basis function expansion for modeling unknown nonlinear processes. First-order Takagi-Sugeno fuzzy model and the analysis of variance (ANOVA) decomposition are combined for the fuzzy local linearization of nonlinear systems, in which B-splines are used as membership functions of the fuzzy sets for input space partition. A modified algorithm for adaptive spline modeling of observation data (MASMOD) is developed for determining the number of necessary B-splines and their knot positions to achieve parsimonious models. This paper illustrates that fuzzy local linearization models have several advantages over local basis function expansion based models in nonlinear system modeling