System identification: theory for the user
System identification: theory for the user
Structure identification of fuzzy model
Fuzzy Sets and Systems
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Identification of fuzzy relational model and its application to control
Fuzzy Sets and Systems
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Theoretical aspects of fuzzy control
Fuzzy engineering
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Identification of linguistic fuzzy models based on learning
Fuzzy model identification
Fuzzy Control and Fuzzy Systems
Fuzzy Control and Fuzzy Systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Complex systems modeling via fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simplifying fuzzy rule-based models using orthogonal transformationmethods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
A highly interpretable form of Sugeno inference systems
IEEE Transactions on Fuzzy Systems
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
Fuzzy Modeling Based on Ordinary Fuzzy Partitions and Nearest Neighbor Clustering
Journal of Intelligent and Robotic Systems
International Journal of Approximate Reasoning
T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm
Engineering Applications of Artificial Intelligence
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
A new T-S fuzzy-modeling approach to identify a boiler-turbine system
Expert Systems with Applications: An International Journal
H∞ control for fuzzy singularly perturbed systems
Fuzzy Sets and Systems
Qualitative modeling of dynamical systems employing continuous-time recurrent fuzzy systems
Fuzzy Sets and Systems
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
This work presents a method for non-linear fuzzy model identification. The main characteristic of the method is the automatic determination of the number and position of the fuzzy sets in the domain of each variable. The resultant fuzzy rule base allows model interpretation by domain experts. The main contribution of this work is a formulation that allows the optimization of output parameters by a least-squares error (LSE) minimization. A numerical solution of the LSE problem is developed based on the singular value decomposition of the regressor matrix. The whole methodology is applied to some numerical examples found in the literature.