NDLI: A Recurrent Functional-Link-Based Neural Fuzzy System and Its Applications

Content Provider	IEEE Xplore Digital Library
Author	Cheng-Hung Chen Cheng-Jian Lin Chin-Teng Lin
Copyright Year	2007
Description	Author affiliation: Dept. of Electr. & Control Eng., National Chiao-Tung Univ., Hsinchu (Cheng-Hung Chen)
Abstract	In this paper, a recurrent functional-link-based neural fuzzy system (RFLNFS) is proposed for prediction of time sequence and skin color detection. The proposed RFLNFS model uses functional link neural network as the consequent part of fuzzy rules. The RFLNFS model can generate the consequent part of a nonlinear combination of the input variables. The recurrent network is embedded in the RFLNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. Finally, the RFLNFS is applied to two simulations. The simulation results of the dynamic system modeling have shown that the RFLNFS model can solve the temporal problem and the RFLNFS model has superior performance than other models
Starting Page	415
Ending Page	420
File Size	372719
Page Count	6
File Format	PDF
ISBN	1424407079
DOI	10.1109/CIISP.2007.369205
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Input variables Neural networks Signal processing algorithms Fuzzy neural networks Polynomials Partitioning algorithms Neurofeedback Fuzzy reasoning Fuzzy systems
Content Type	Text
Resource Type	Article

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