Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 58, Issue 12, Pages 5438-5450Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2011.2164773
Keywords
Adaptive control; fuzzy inference systems; neural networks; online learning; radial basis function (RBF) networks
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Radial basis function (RBF) networks have advantages of easy design, good generalization, strong tolerance to input noise, and online learning ability. The properties of RBF networks make it very suitable to design flexible control systems. This paper presents a review on different approaches of designing and training RBF networks. The recently developed algorithm is introduced for designing compact RBF networks and performing efficient training process. At last, several problems are applied to test the main properties of RBF networks, including their generalization ability, tolerance to input noise, and online learning ability. RBF networks are also compared with traditional neural networks and fuzzy inference systems.
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