NDLI: Supervised and unsupervised learning with fuzzy similarity for neural-network-based fuzzy logic control systems

Content Provider	IEEE Xplore Digital Library
Author	Lee, C.S.G. Lin, C.T.
Copyright Year	1992
Description	Author affiliation: Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA (Lee, C.S.G.)
Abstract	A feedforward multilayered connectionist network that has distributed learning abilities is proposed to realize the basic elements and functions of a traditional fuzzy logic controller. Two complementary structure/parameter learning algorithms are proposed for setting up the proposed neural-network-based fuzzy logic control system (NN-FLCS). First, a two-phase hybrid learning algorithm is proposed which combines unsupervised and supervised learning procedures to build the rule nodes and train the membership functions. The two-phase hybrid learning algorithm performs well if sets of training data are available offline. The authors then propose an online supervised structure/parameter learning algorithm for constructing the NN-FLCS dynamically. This algorithm combines the backpropagation learning scheme for the parameter learning and a fuzzy similarity measure for the structure learning. The proposed online structure/parameter learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Computer simulation examples are presented to illustrate the performance of the learning algorithms.
Starting Page	688
Ending Page	693
File Size	613052
Page Count	6
File Format	PDF
ISBN	0780307208
DOI	10.1109/ICSMC.1992.271691
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-10-18
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Unsupervised learning Control systems Automatic control Partitioning algorithms Supervised learning Training data Heuristic algorithms Fuzzy sets Fuzzy control
Content Type	Text
Resource Type	Article

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