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Method for neuro-fuzzy inference system learning for ICE tests

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dc.date.accessioned 2019-01-22T20:52:16Z
dc.date.available 2019-01-22T20:52:16Z
dc.date.issued 2018
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/149183
dc.description.abstract © 2018, Institute of Advanced Scientific Research, Inc. All rights reserved. The application of an intelligent model for tuning an automated test system for diesel engines is considered. A neuro-fuzzy network has been designed to produce a control effect on the diesel. A technique for designing a knowledge base for controlling the operating modes of a diesel engine during its testing has been developed. In the life cycle of products, including internal combustion engines (ICE), a significant place is occupied by various technological tests of both individual units and the engine as a whole. Modern requirements to constant increase of technical level of let out designs result in that the share of expenses for carrying out of tests of diesel engines at creation of new samples all more increases. Especially large these costs become when the levels of automation of production and scientific research work do not match. In connection with this automation and mechanization of technological trials is one of the main tasks of increasing the technological level of production and the quality of the parts produced.
dc.subject Automated test system
dc.subject Automated test technology
dc.subject Diesel engine
dc.subject Fuzzy rules
dc.subject Information technology
dc.subject Knowledge base
dc.subject Neuro-fuzzy network
dc.subject Stand
dc.subject Test
dc.title Method for neuro-fuzzy inference system learning for ICE tests
dc.type Article
dc.relation.ispartofseries-issue 10 Special Issue
dc.relation.ispartofseries-volume 10
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 1773
dc.source.id SCOPUS-2018-10-10-SID85054031826


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  • Публикации сотрудников КФУ Scopus [22633]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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