Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
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
A novel hybrid algorithm for function approximation
Expert Systems with Applications: An International Journal
Information granulation as a basis of fuzzy modeling
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions
Expert Systems with Applications: An International Journal
T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm
Engineering Applications of Artificial Intelligence
A new probabilistic fuzzy model: Fuzzification--Maximization (FM) approach
International Journal of Approximate Reasoning
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
Rule weights in a neuro-fuzzy system with a hierarchical domain partition
International Journal of Applied Mathematics and Computer Science
Information Sciences: an International Journal
Thermal modeling of power transformers using evolving fuzzy systems
Engineering Applications of Artificial Intelligence
Linguistic fuzzy model identification based on PSO with different length of particles
Applied Soft Computing
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
A hierarchical procedure for the synthesis of ANFIS networks
Advances in Fuzzy Systems
Engineering Applications of Artificial Intelligence
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper presents an explanation of a fuzzy model considering the correlation among components of input data. Generally, fuzzy models have a capability of dividing an input space into several subspaces compared to a linear model. But hitherto suggested fuzzy modeling algorithms have not taken into consideration the correlation among components of sample data and have addressed them independently, which results in an ineffective partition of the input space. In order to solve this problem, this paper proposes a new fuzzy modeling algorithm, which partitions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among components of sample data. As a way to use the correlation and divide the input space, the method of principal component is used. Finally, the results of the computer simulation are given to demonstrate the validity of this algorithm