CO M P U TAT IO N AL I NT ELL IG EN C E AP P LIED TO SIGNAL PROCESSING: A PROPOSAL FOR FUZZY NEURAL IDENTIFICATION
Celso Pascoli · 2004
In this study an approach to fuzzy neural identification of MIMO discrete-time nonlinear dynamical systems is proposed. Based on the Takagi-Sugeno (TS) fuzzy neural network, off-line and on-line schemes are formulated as a NARX (Nonlinear Au- toRegressive with exogenous input) fuzzy neural model from sam- ples of a nonlinear dynamical system w here the consequent param- etcrs are modified by an adaptive WIV (Weighted Instrumental Variable) algorithm based on the numerically robust orthogonal Householder transformation.