Computational intelligence applied to signal processing: a proposal for fuzzy neural identification

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 autoregressive with exogenous input) fuzzy neural model from samples of a nonlinear dynamical system where the consequent parameters are modified by an adaptive WIV (weighted instrumental variable) algorithm based on the numerically robust orthogonal householder transformation