Construction of fuzzy models through clustering techniques
Fuzzy Sets and Systems
Fuzzy system modeling by fuzzy partition and GA hybrid schemes
Fuzzy Sets and Systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A Robust Clustering Algorithm Based on Competitive Agglomeration and Soft Rejection of Outliers
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A heuristic error-feedback learning algorithm for fuzzy modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Evolutionary design of fuzzy rule base for nonlinear system modeling and control
IEEE Transactions on Fuzzy Systems
Supervised fuzzy clustering for rule extraction
IEEE Transactions on Fuzzy Systems
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In this paper, A new identification method for nonlinear system model from input-output data is presented. In accordance with the problem that sensitivity to initialization and noise, and some relative parameters must be determined beforehand during the fuzzy clustering process in the usual fuzzy cluster algorithm, and the existing competitive clustering algorithm have poor convergence properties, and make convergence to a local minimum more likely. A type of adaptive competitive cluster algorithm for structure identification is presented. At the same time, orthogonal least squares (OLS) method algorithm is used to remove redundant fuzzy rules and identify model parameters during the clustering process. Through simulation research, the effectiveness of the method is proved.