NDLI: A recurrent neural fuzzy network controller for a temperature control system

Content Provider	IEEE Xplore Digital Library
Author	Chia-Feng Juang Jung-Shing Chen
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng., Nat. Chung-Hsing Univ., Taichung, Taiwan (Chia-Feng Juang; Jung-Shing Chen)
Abstract	Temperature control by a TSK-type Recurrent Neural Fuzzy Network (TRNFN) controller based on the direct inverse control configuration is proposed in this paper. The TRNFN is a recurrent fuzzy network developed from a series of TSK type fuzzy if-then rules, and is on-line constructed by concurrent structure/parameter learning. The TRNFN has the following advantages when applied to temperature control problems (1) high learning ability, which considerably reduces the controller training time, (2) no a priori knowledge of the plant order is required, which eases the design process, (3) high control performance. These advantages are verified by applying TRNFN to a real water bath temperature control plant, where the performance of a backpropagation neural network is compared.
Sponsorship	IEEE IEEE Neurla Networks Soc
Starting Page	408
Ending Page	413
File Size	342943
Page Count	6
File Format	PDF
ISBN	0780378105
DOI	10.1109/FUZZ.2003.1209398
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature control Fuzzy control Fuzzy neural networks Fuzzy systems Control systems Neural networks Neurofeedback Fuzzy logic Convergence Industrial training
Content Type	Text
Resource Type	Article

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