On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling
Fuzzy Sets and Systems
An adaptive neuro-fuzzy system for efficient implementations
Information Sciences: an International Journal
SEGMENTATION OF MULTIPLE HUMAN OBJECTS IN VIDEO SEQUENCES
Applied Artificial Intelligence
Hybrid Intelligent Modeling Approach for the Ball Mill Grinding Process
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Hybrid learning-based neuro-fuzzy inference system: a new approach for system modeling
International Journal of Systems Science
Vector Quantization of Images Using a Fuzzy Clustering Method
Cybernetics and Systems
Adaptive growing-and-pruning neural network control for a linear piezoelectric ceramic motor
Engineering Applications of Artificial Intelligence
T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm
Engineering Applications of Artificial Intelligence
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 probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
Forecasting coal and rock dynamic disaster based on adaptive neuro-fuzzy inference system
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
Engineering Applications of Artificial Intelligence
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We propose an approach for neuro-fuzzy system modeling. A neuro-fuzzy system for a given set of input-output data is obtained in two steps. First, the data set is partitioned automatically into a set of clusters based on input-similarity and output-similarity tests. Membership functions associated with each cluster are defined according to statistical means and variances of the data points included in the cluster. Then, a fuzzy IF-THEN rule is extracted from each cluster to form a fuzzy rule-base. Second, a fuzzy neural network is constructed accordingly and parameters are refined to increase the precision of the fuzzy rule-base. To decrease the size of the search space and to speed up the convergence, we develop a hybrid learning algorithm which combines a recursive singular value decomposition-based least squares estimator and the gradient descent method. The proposed approach has advantages of determining the number of rules automatically and matching membership functions closely with the real distribution of the training data points. Besides, it learns faster, consumes less memory, and produces lower approximation errors than other methods.