Input-output modelling with decomposed neuro-fuzzy ARX model
Neurocomputing
An adaptive neuro-fuzzy system for efficient implementations
Information Sciences: an International Journal
An optimal T-S model for the estimation and identification of nonlinear functions
WSEAS Transactions on Systems and Control
New optimal approach for the identification of Takagi-Sugeno fuzzy model
CONTROL'08 Proceedings of the 4th WSEAS/IASME international conference on Dynamical systems and control
A Flexible Neuro-Fuzzy Autoregressive Technique for Non-linear Time Series Forecasting
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Control of a rope-driven self-leveling device for leveling adjustment
ACC'09 Proceedings of the 2009 conference on American Control Conference
An affine fuzzy model with local and global interpretations
Applied Soft Computing
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
Robust PID TS fuzzy control methodology based on gain and phase margins specifications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, a new algorithm for neuro-fuzzy identification of multivariable discrete-time nonlinear dynamic systems, more specifically applied to consequent parameters estimation of the neuro-fuzzy inference system, is proposed based on a decomposed form as a set of coupled multiple input and single output (MISO) Takagi-Sugeno (TS) neuro-fuzzy networks. An on-line scheme is formulated for modeling a nonlinear autoregressive with exogenous input (NARX) recurrent neuro-fuzzy structure from input-output samples of a multivariable nonlinear dynamic system in a noisy environment. The adaptive weighted instrumental variable (WIV) algorithm by QR factorization based on the numerically robust orthogonal Householder transformation is developed to modify the consequent parameters of the TS multivariable neuro-fuzzy network