An optimal T-S model for the estimation and identification of nonlinear functions
WSEAS Transactions on Systems and Control
New optimal approach for the identification of Takagi-Sugeno fuzzy model
CONTROL'08 Proceedings of the 4th WSEAS/IASME international conference on Dynamical systems and control
Input selection in learning systems: a brief review of some important issues and recent developments
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A hybrid neuro-fuzzy approach for spinal force evaluation in manual materials handling tasks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Classifying the risk of work related low back disorders due to manual material handling tasks
Expert Systems with Applications: An International Journal
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Identification of the significance of input variables is very important for complex systems with high-dimensional input space. In this paper, a method using fuzzy average with fuzzy cluster distribution is proposed. To avoid the interference of different distributions of the sampling data, the distribution of fuzzy clusters in the sampling data is considered, instead of the original data set. To discover the input-output relationship, the methods of fuzzy rules and fuzzy C-means are first used to partition the original sampling data set into fuzzy clusters. A new data set with the same distribution of the fuzzy clusters is produced. The fuzzy average method is then applied to the new data set. By doing so, the interference of distribution of the original sampling data is removed. This method is straightforward and computationally easy. The performance is tested on both benchmark data and real-world data.