Fuzzy identification from a grey box modeling point of view
Fuzzy model identification
Robust Solution to Fuzzy Identification Problem with Uncertain Data by Regularization
Fuzzy Optimization and Decision Making
Robust Adaptive Identification of Fuzzy Systems with Uncertain Data
Fuzzy Optimization and Decision Making
H∞ optimality of the LMS algorithm
IEEE Transactions on Signal Processing
Rule-based modeling: precision and transparency
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Function approximation using fuzzy neural networks with robust learning algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
A robust design criterion for interpretable fuzzy models with uncertain data
IEEE Transactions on Fuzzy Systems
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
Fuzzy filtering for robust bioconcentration factor modelling
Environmental Modelling & Software
Brief paper: Experience-consistent modeling: Regression and classification problems
Automatica (Journal of IFAC)
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
Variational bayes for a mixed stochastic/deterministic fuzzy filter
IEEE Transactions on Fuzzy Systems
| Hi-index | 22.15 |
A novel method for the robust identification of interpretable fuzzy models, based on the criterion that identification errors are least sensitive to data uncertainties and modelling errors, is suggested. The robustness of identification errors towards unknown disturbances (data uncertainties, modelling errors, etc.) is achieved by bounding (i.e. minimizing) the maximum possible value of energy-gain from disturbances to the identification errors. The solution of energy-gain bounding problem, being robust, shows an improved performance of the identification method. The flexibility of the proposed framework is shown by designing the variable learning rate identification algorithms in both deterministic and stochastic frameworks.