An improved fuzzy neural network based on T-S model
Expert Systems with Applications: An International Journal
Artificial wavelet neural network and its application in neuro-fuzzy models
Applied Soft Computing
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
Fuzzy Sets and Systems
Adaptive noise cancellation with computational-intelligence-based approach
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
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We develop a neurofuzzy network technique to extract TSK-type fuzzy rules from a given set of input-output data for system modeling problems. Fuzzy clusters are generated incrementally from the training dataset, and similar clusters are merged dynamically together through input-similarity, output-similarity, and output-variance tests. The associated membership functions are defined with statistical means and deviations. Each cluster corresponds to a fuzzy IF-THEN rule, and the obtained rules can be further refined by a fuzzy neural network with a hybrid learning algorithm which combines a recursive singular value decomposition-based least squares estimator and the gradient descent method. The proposed technique has several advantages. The information about input and output data subspaces is considered simultaneously for cluster generation and merging. Membership functions match closely with and describe properly the real distribution of the training data points. Redundant clusters are combined, and the sensitivity to the input order of training data is reduced. Besides, generation of the whole set of clusters from the scratch can be avoided when new training data are considered.