Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identification of functional fuzzy models using multidimensional reference fuzzy sets
Fuzzy Sets and Systems
A clustering algorithm for fuzzy model identification
Fuzzy Sets and Systems
About the use of fuzzy clustering techniques for fuzzy model identification
Fuzzy Sets and Systems
A unified parameterized formulation of reasoning in fuzzy modeling and control
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Fuzzy least-squares linear regression analysis for fuzzy input-output data
Fuzzy Sets and Systems - Information processing
Fuzzy system modeling in pharmacology: an improved algorithm
Fuzzy Sets and Systems - Fuzzy models
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
Complex systems modeling via fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ϵ-insensitive fuzzy c-regression models: introduction to ϵ-insensitive fuzzy modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Development of a systematic methodology of fuzzy logic modeling
IEEE Transactions on Fuzzy Systems
Takagi-Sugeno fuzzy modeling incorporating input variables selection
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
FUZZYSS’2009: 1st International Fuzzy Systems Symposium,1-2 October 2009, Ankara, Turkey
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS’2009
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In this study, we propose fuzzy modeling algorithm to improve Takagi-Sugeno fuzzy model. This algorithm initially finds desirable number of rules at once, in advance, and then identifies the premise and consequent parameters separately by fixing number determined. The proposed algorithm consists of three stages: determination of the optimal number of fuzzy rules, coarse tuning of parameters and fine tuning of these parameters. To find the optimal number of rules, the new cluster validity algorithm that is based on the validity criterion V sv adapted to the usage of FCRM-like clustering, is proposed. In coarse tuning, by using the mentioned clustering algorithm for input-output data and the projection scheme, the consequent and premise parameters are coarsely defined. In fine tuning, the gradient descent (GD) method is used to precisely adjust parameters of fuzzy model but unlike other similar modeling algorithms, the premise parameters are adjusted with respect to multidimensional membership function in premise part of rule. Finally, two examples are given to demonstrate the validity of suggested modeling algorithm and show its excellent predictive performance.