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Neuro-fuzzy models for systems identification applied to the operation of nuclear power plants; Sistemas neuro-fuzzy para identificacao de sistemas aplicados a operacao de centrais nucleares

Abstract

A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new  More>>
Publication Date:
Sep 01, 2000
Product Type:
Thesis/Dissertation
Report Number:
INIS-BR-3952
Resource Relation:
Other Information: TH: Tese (Ph.D.); 52 refs., 26 figs., 13 tabs., 56 graphs; PBD: Sep 2000
Subject:
22 GENERAL STUDIES OF NUCLEAR REACTORS; CHAOS THEORY; COMPUTER CALCULATIONS; COMPUTERIZED CONTROL SYSTEMS; COMPUTERIZED SIMULATION; FUZZY LOGIC; MATHEMATICAL MODELS; NONLINEAR PROGRAMMING; NUCLEAR POWER PLANTS; OPERATION; PRESSURIZERS; PROBABILITY; PWR TYPE REACTORS
OSTI ID:
20528906
Research Organizations:
Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Programa de Engenharia Nuclear
Country of Origin:
Brazil
Language:
Portuguese
Other Identifying Numbers:
TRN: BR0444013095176
Availability:
Available from INIS in electronic form
Submitting Site:
BRN
Size:
174 pages
Announcement Date:

Citation Formats

Alves, Antonio Carlos Pinto Dias. Neuro-fuzzy models for systems identification applied to the operation of nuclear power plants; Sistemas neuro-fuzzy para identificacao de sistemas aplicados a operacao de centrais nucleares. Brazil: N. p., 2000. Web.
Alves, Antonio Carlos Pinto Dias. Neuro-fuzzy models for systems identification applied to the operation of nuclear power plants; Sistemas neuro-fuzzy para identificacao de sistemas aplicados a operacao de centrais nucleares. Brazil.
Alves, Antonio Carlos Pinto Dias. 2000. "Neuro-fuzzy models for systems identification applied to the operation of nuclear power plants; Sistemas neuro-fuzzy para identificacao de sistemas aplicados a operacao de centrais nucleares." Brazil.
@misc{etde_20528906,
title = {Neuro-fuzzy models for systems identification applied to the operation of nuclear power plants; Sistemas neuro-fuzzy para identificacao de sistemas aplicados a operacao de centrais nucleares}
author = {Alves, Antonio Carlos Pinto Dias}
abstractNote = {A nuclear power plant has a myriad of complex system and sub-systems that, working cooperatively, make the control of the whole plant. Nevertheless their operation be automatic most of the time, the integral understanding of their internal- logic can be away of the comprehension of even experienced operators because of the poor interpretability those controls offer. This difficulty does not happens only in nuclear power plants but in almost every a little more complex control system. Neuro-fuzzy models have been used for the last years in a attempt of suppress these difficulties because of their ability of modelling in linguist form even a system which behavior is extremely complex. This is a very intuitive human form of interpretation and neuro-fuzzy model are gathering increasing acceptance. Unfortunately, neuro-fuzzy models can grow up to become of hard interpretation because of the complexity of the systems under modelling. In general, that growing occurs in function of redundant rules or rules that cover a very little domain of the problem. This work presents an identification method for neuro-fuzzy models that not only allows models grow in function of the existent complexity but that beforehand they try to self-adapt to avoid the inclusion of new rules. This form of construction allowed to arrive to highly interpretative neuro-fuzzy models even of very complex systems. The use of this kind of technique in modelling the control of the pressurizer of a PWR nuclear power plant allowed verify its validity and how neuro-fuzzy models so built can be useful in understanding the automatic operation of a nuclear power plant. (author)}
place = {Brazil}
year = {2000}
month = {Sep}
}