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

Published 2 January 2003

C.S.G. Lee, Chin‐Teng Lin

DOI10.1109/icsmc.1992.271691

Citations25

TL;DR

An online supervised structure/parameter learning algorithm is proposed which can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously and performs well if sets of training data are available offline.

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.>

Keywords

Computer ScienceBiochemistry, Genetics and Molecular Biology

Springer series in information sciencesSelf-Organization and Associative Memory

8,770 Citations1989Teuvo Kohonen

IEEE Transactions on Systems Man and CyberneticsFuzzy logic in control systems: fuzzy logic controller. I

5,581 Citations1990C.C. Lee

This paper presents a survey of the FLC; describes a general methodology for constructing an FLC and assessing its per formance; and points to problems which need further research.

The MIT Press eBooksParallel Distributed Processing

3,227 Citations1987James L. McClelland, David E. Rumelhart +1 more

ComputerFuzzy logic

1,730 Citations1988L. A. Zadeh

IEEE Transactions on ComputersNeural-network-based fuzzy logic control and decision system

1,478 Citations1991Chin‐Teng Lin, C.S.G. Lee

A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed, in the form of feedforward multilayer net, which avoids the rule-matching time of the inference engine in the traditional fuzzy logic system.

Cognitive ScienceFeature Discovery by Competitive Learning*

1,073 Citations1985David E. Rumelhart, David Zipser

This paper shows how a set of feature detectors which capture important aspects of the set of stimulus input patterns are discovered and how these feature detectors form the basis of a multilayer system that serves to learn categorizations of stimulus sets which are not linearly separable.

View PDF

Cognitive ScienceFeature discovery by competitive learning

890 Citations1985David E. Rumelhart, David Zipser

Fuzzy Sets and SystemsFuzzy control of model car

439 Citations1985Michio Sugeno, Masayuki Nishida

Elsevier eBooksADAPTIVE INFERENCE IN FUZZY KNOWLEDGE NETWORKS

134 Citations1993Bart Kosko

An appropriate causal learning law for inductively inferring FCMs from data is the differential Hebbian law, which modifies causal connections by correlating time derivatives of FCM node outputs.

Real-time supervised structure/parameter learning for fuzzy neural network

89 Citations2003Chin‐Teng Lin, C.S.G. Lee

A real-time supervised structure and parameter learning algorithm for constructing fuzzy neural networks (FNNs) automatically and dynamically is proposed which combines the backpropagation learning scheme for the parameter learning and a novel fuzzy similarity measure for the structure learning.