A fast and compact fuzzy neural network for online extraction of fuzzy rules

Authors:
Ning Wang;Xianyao Meng;Yiming Bai
Affiliations:
Institute of Automation, Dalian Maritime University, Dalian, China;Institute of Automation, Dalian Maritime University, Dalian, China;Institute of Automation, Dalian Maritime University, Dalian, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

A novel paradigm termed fast and compact fuzzy neural network (FCFNN), which incorporates a pruning strategy into some growing criteria, is proposed for online extraction of fuzzy rules. The proposed growing criteria not only speed up the online learning process but also result in a parsimonious fuzzy neural network while achieving comparable performance and accuracy by virtue of the growing and pruning mechanism. The FCFNN starts with no hidden neurons and parsimoniously generates new hidden units according to the proposed growing criteria as learning proceeds. In the second learning phase, all free parameters of the hidden units are updated by the extended Kalman filter (EKF) method. The performance of the FCFNN algorithm is compared with other popular algorithms like ANFIS, GDFNN and SOFNN, etc., for nonlinear function approximation. Simulation results demonstrate that the learning speed of the proposed FCFNN algorithm is faster and the network structure is more compact while comparable generalization performance and accuracy are achieved, moreover, it is capable of extracting fuzzy rules online.