NDLI: A New Learning Algorithm for a Fully Connected Neuro-Fuzzy Inference System

Content Provider	IEEE Xplore Digital Library
Author	Chen, C.L.P. Jing Wang Chi-Hsu Wang Long Chen
Copyright Year	2012
Abstract	A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.
Starting Page	1741
Ending Page	1757
Page Count	17
File Size	3894657
File Format	PDF
ISSN	2162237X
Volume Number	25
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Input variables Heuristic algorithms Training Neurons Convergence Inference algorithms optimal learning. Fully connected neuro-fuzzy inference systems (F-CONFIS) fuzzy logic fuzzy neural networks gradient descent neural networks (NNs) neuro-fuzzy system optimal learning
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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