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
A simply identified Sugeno-type fuzzy model via double clustering
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on modeling with soft-computing
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Hybrid fuzzy modeling of chemical processes
Fuzzy Sets and Systems - Fuzzy models
Structure identification and parameter optimization for non-linear fuzzy modeling
Fuzzy Sets and Systems - Fuzzy systems
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
A transformed input-domain approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Enhanced combination modeling method for combustion efficiency in coal-fired boilers
Applied Soft Computing
Adaptive fuzzy identification and predictive control for industrial processes
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In order to build accurate model for complicated nonlinear system in engineering, like boiler-turbine system, a novel fuzzy-modeling approach is proposed, which is based on a new fuzzy c-regression model (NFCRM) clustering algorithm and is able to determine the right number of rules automatically. In this method, NFCRM is applied to build the fuzzy structure and then identify the premise parameters; a new criterion is proposed to auto determine the number of rules in fuzzy modeling; after the fuzzy rules have been decided, orthogonal least square is exploited to identify the consequent parameters. Simulation examples are given to demonstrate the validity of the proposed modeling approach, and the results show the new approach is very efficient with high accuracy. Finally, the new approach is applied in fuzzy modeling of a typical boiler-turbine system successfully.