Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
Hybrid fuzzy polynomial neural networks
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Identification of piecewise affine systems by means of fuzzy clustering and competitive learning
Engineering Applications of Artificial Intelligence
Knowledge discovery by a neuro-fuzzy modeling framework
Fuzzy Sets and Systems
Regional models for nonlinear system identification using the self-organizing map
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Modeling nonlinear systems by neural networks and fuzzy systems encounters problems such as the conflict between overfitting and good generalization and low reliability, which requires a great number of fuzzy rules or neural nodes and uses very complicated learning algorithms. A new adaptive fuzzy inference system, combined with a learning algorithm, is proposed to cope with these problems. First, the algorithm partitions the input space into some local regions by competitive learning, then it determines the decision boundaries for local input regions, and finally, based on the decision boundaries, it learns the fuzzy rule for each local region by recursive least squares (RLS). In the learning algorithm, the key role of the decision boundaries is highly emphasized. To demonstrate the validity of the proposed learning approach and the new adaptive fuzzy inference system, four examples are studied by the proposed method and compared with the previous results