NDLI: On the Equivalence of a Table Lookup (TL) Technique and Fuzzy Neural Network (FNN) With Block Pulse Membership Functions (BPMFs) and Its Application to Water Injection Control of an Automobile

Content Provider	IEEE Xplore Digital Library
Author	Chi-Hsu Wang Jung-Sheng Wen
Copyright Year	1998
Abstract	This paper presents an alternative method to design a fuzzy neural network (FNN) using a set of nonoverlapped block pulse membership functions (BMPFs), and this FNN with nonoverlapped BPMFs will be shown to be equivalent to the conventional table lookup (TL) technique. Therefore, the hidden links between TL and FNN techniques are revealed in this paper that provides a methodology to design a TL controller based on the FNN design concept. In order to do so, a new direct formula is first developed to generate the fuzzy rules from the premise part in FNN. This direct formula not only guarantees a one-to-one mapping that maps the fuzzy membership functions onto the fuzzy rules, but also alleviates the coding effort during hardware implementation. It is further elaborated that the FNN with nonoverlapped BPMFs has the advantage of faster online training that requires less computation time, but at the cost of more memory requirement to store the fuzzy rules. The application of this new approach has been applied successfully in the water injection control of a turbo-charged automobile with excellent results.
Starting Page	574
Ending Page	580
Page Count	7
File Size	359070
File Format	PDF
ISSN	10946977
Volume Number	38
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Fuzzy control Table lookup Educational institutions Automobiles Neural networks Hardware Control engineering Design methodology Image coding table lookup (TL) controller Fuzzy neural network (FNN) membership functions (MFs) optimal training
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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