Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/4797
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSingh, Jagdev-
dc.contributor.authorSingh, Nirmal-
dc.contributor.authorSharma, J K-
dc.date.accessioned2009-06-26T04:49:16Z-
dc.date.available2009-06-26T04:49:16Z-
dc.date.issued2006-01-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://hdl.handle.net/123456789/4797-
dc.description22-30en_US
dc.description.abstractFuzzy modeling and mathematical analysis for the intelligent control of compressor in order to regulate refrigerant mass flow in vapour compression refrigeration system has been described. The compressor speed and delivery pressure are taken as input parameters and mass flow rate is considered as output parameter. To develop an effective model for control design, fuzzy rule base is designed with and without sub clustering. Intelligent control is used to receive the input signal and generate the output signal to control the mass flow rate from the compressor. Swept volume, suction pressure and densities are considered as fixed parameters.en_US
dc.language.isoen_USen_US
dc.publisherCSIRen_US
dc.relation.ispartofseriesG06N7/02; F25Ben_US
dc.sourceJSIR Vol.65(01) [January 2006]en_US
dc.subjectAdaptive neuro-fuzzy inference system (ANFIS)en_US
dc.subjectClusteringen_US
dc.subjectFuzzy controlen_US
dc.subjectRefrigeration compressoren_US
dc.subjectSugeno fuzzy inference systemen_US
dc.titleFuzzy modeling and identification of intelligent control for refrigeration compressoren_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.65(01) [January 2006]

Files in This Item:
File Description SizeFormat 
JSIR 65(1) 22-30.pdf820.47 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.