Result: Refinement of a fuzzy control rule set
Title:
Refinement of a fuzzy control rule set
Authors:
Source:
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Mathware & soft computing; 1998: Vol.: 5 Núm.: 2-3
Universitat Politècnica de Catalunya (UPC)
Mathware & soft computing; 1998: Vol.: 5 Núm.: 2-3
Publisher Information:
Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica, 1998.
Publication Year:
1998
Subject Terms:
theory refinement, Classificació AMS::90 Operations research, Computing methodologies and applications, Programació lògica, Fuzzy control/observation systems, mathematical programming::90C Mathematical programming, Learning and adaptive systems in artificial intelligence, Theory refinemrnt, 90 Operations research, mathematical programming::90C Mathematical programming [Classificació AMS], fuzzy logic controller, Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming, SLAVE, Fuzzy logic, machine learning, Inductive learning algorithm, Machine learning, Programació (Matemàtica), system modeling, System modelling, fuzzy logic
Document Type:
Academic journal
Article
File Description:
application/pdf; application/xml; text/html
Language:
English
Access URL:
Rights:
CC BY NC ND
Accession Number:
edsair.dedup.wf.002..fd0832d06d33c3e6d93ab04465ce1d3b
Database:
OpenAIRE
Further Information
Fuzzy logic controller performance depends on the fuzzy control rule set. This set can be obtained either by an expert or from a learning algorithm through a set of examples. Recently, we have developed SLAVE an inductive learning algorithm capable of identifying fuzzy systems. The refinement of the rules proposed by SLAVE (or by an expert) can be very important in order to improve the accuracy of the model and in order to simplify the description of the system. The refinement algorithm is based on an heuristic process of generalization, specification, addition and elimination of rules.