NDLI: Designing compact fuzzy rule-based systems with default hierarchies for linguistic approximation

Content Provider	IEEE Xplore Digital Library
Author	Ishibuchi, H. Nakashima, T.
Copyright Year	1999
Description	Author affiliation: Dept. of Ind. Eng., Osaka Prefecture Univ., Japan (Ishibuchi, H.)
Abstract	This paper illustrates how a genetic algorithm can be employed for designing a compact fuzzy rule-based system, which linguistically describes a nonlinear function with many inputs in a human understandable manner. First we show that general fuzzy if-then rules with only a few antecedent conditions are necessary for such linguistic modeling when nonlinear functions have many input variables. Next we illustrate a new fuzzy reasoning method for handling fuzzy if-then rules with different specificity levels (i.e., for handling a mixture of general and specific fuzzy if-then rules). The fuzzy reasoning method is formulated based on the concept of default hierarchies of Holland et al.(1986) for calculating output values in a similar manner to human thinking. Then we formulate a rule selection problem for finding a small number of relevant fuzzy if-then rules among a large number of possible combinations of antecedent and consequent linguistic values. The rule selection problem has two objectives: to minimize the prediction error and to minimize the number of selected rules. A genetic algorithm is applied to the rule selection problem. Finally we suggest how genetics-based machine learning approaches (i.e., Pittsburgh and Michigan) can be used for linguistic modeling of nonlinear functions.
Starting Page	2341
Ending Page	2348
File Size	837612
Page Count	8
File Format	PDF
ISBN	0780355369
DOI	10.1109/CEC.1999.785566
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy systems Knowledge based systems Humans Input variables Fuzzy neural networks Fuzzy sets Algorithm design and analysis Industrial engineering Genetic algorithms Neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Linguistic modeling for function approximation using grid partitions

Genetic-algorithm-based approach to linguistic approximation of nonlinear functions with many input variables

A multiobjective genetic algorithm for feature selection and granularity learning in fuzzy-rule based classification systems

Designing Fast and Accurate Fuzzy Approximators with Kohonen Networks and Genetic Algorithms

Multi-objective evolutionary design of fuzzy rule-based systems

Designing fuzzy rule-based classifiers that can visually explain their classification results to human users

Knowledge extraction from hierarchical fuzzy model obtained by fuzzy neural networks and genetic algorithm

Toward evolving consistent, complete, and compact fuzzy rule sets for classification problems

Linguistic rule extraction from neural networks and genetic-algorithm-based rule selection

Designing compact fuzzy rule-based systems with default hierarchies for linguistic approximation

Similar Documents

Linguistic modeling for function approximation using grid partitions

Genetic-algorithm-based approach to linguistic approximation of nonlinear functions with many input variables

A multiobjective genetic algorithm for feature selection and granularity learning in fuzzy-rule based classification systems

Designing Fast and Accurate Fuzzy Approximators with Kohonen Networks and Genetic Algorithms

Multi-objective evolutionary design of fuzzy rule-based systems

Designing fuzzy rule-based classifiers that can visually explain their classification results to human users

Knowledge extraction from hierarchical fuzzy model obtained by fuzzy neural networks and genetic algorithm

Toward evolving consistent, complete, and compact fuzzy rule sets for classification problems

Linguistic rule extraction from neural networks and genetic-algorithm-based rule selection

Designing compact fuzzy rule-based systems with default hierarchies for linguistic approximation