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Author Yan-Qing Zhang
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2000
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Neural networks ♦ Fuzzy neural networks ♦ Fuzzy sets ♦ Bismuth ♦ Computer science ♦ Training data ♦ Fuzzy systems ♦ Relational databases
Abstract An important task of learning is to establish relations among granules such as classes, clusters, sets, groups, etc. The relations can be represented by granular If-Then rules. How to quickly discover the granular If-Then rules becomes a major long-term problem. Conventional training-based approaches such as neural networks and neuro-fuzzy systems have the learning speed bottleneck problem. The new constructive 2-variable granular system was proposed based on soft computing and granular computing to highly speed up granular knowledge discovery. Now the important question is "is the constructive 2-variable granular system a universal approximator?" The constructive 2-variable granular system is proved to be a universal approximator. According to the proof, we can construct a granular constructive a-variable granular system with any required accuracy and a near optimal number of granular rules. In the future, the granular constructive n-variable fuzzy system will be investigated in general.
Description Author affiliation: Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA (Yan-Qing Zhang)
ISBN 0780362748
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2000-07-13
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 339.75 kB
Page Count 5
Starting Page 358
Ending Page 362

Source: IEEE Xplore Digital Library