An Application of Weighted Triangular Norms to Complexity Reduction of Neuro-fuzzy Systems
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
On Differential Stroke Diagnosis by Neuro-fuzzy Structures
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
On the estimation of parameters of Takagi-Sugeno fuzzy filte
IEEE Transactions on Fuzzy Systems
Adaptive fuzzy filtering in a deterministic setting
IEEE Transactions on Fuzzy Systems
A new method for design and reduction of neuro-fuzzy classification systems
IEEE Transactions on Neural Networks
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Variational bayes for a mixed stochastic/deterministic fuzzy filter
IEEE Transactions on Fuzzy Systems
On automatic design of neuro-fuzzy systems
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
On designing of flexible neuro-fuzzy systems for nonlinear modelling
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
An energy-gain bounding approach to robust fuzzy identification
Automatica (Journal of IFAC)
Estimation of heart rate signals for mental stress assessment using neuro fuzzy technique
Applied Soft Computing
Knowledge acquisition based on learning of maximal structure fuzzy rules
Knowledge-Based Systems
Rule base identification in fuzzy networks by Boolean matrix equations
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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We believe that nonlinear fuzzy filtering techniques may be turned out to give better robustness performance than the existing linear methods of estimation (H2 and H∞ filtering techniques), because of the fact that not only linear parameters (consequents), but also the nonlinear parameters (membership functions) attempt to identify the uncertain behavior of the unknown system. However, the fuzzy identification methods must be robust to data uncertainties and modeling errors to ensure that the fuzzy approximation of unknown system's behavior is optimal in some sense. This study presents a deterministic approach to the robust design of fuzzy models in the presence of unknown but finite uncertainties in the identification data. We consider online identification of an interpretable fuzzy model, based on the robust solution of a regularized least-squares fuzzy parameters estimation problem. The aim is to resolve the difficulties associated with the robust fuzzy identification method due to lack of a priori knowledge about upper bounds on the data uncertainties. The study derives an optimal level of regularization that should be provided to ensure the robustness of fuzzy identification strategy by achieving an upper bound on the value of energy gain from data uncertainties and modeling errors to the estimation errors. A time-domain feedback analysis of the proposed identification approach is carried out with emphasis on stability, robustness, and steady-state issues. The simulation studies are provided to show the superiority of the proposed fuzzy estimation over the classical estimation methods.