Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9557
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dc.contributor.authorSHEN, Yongjun-
dc.contributor.authorGu, X.-
dc.contributor.authorBao, Qiaoliang-
dc.date.accessioned2009-04-30T10:36:31Z-
dc.date.issued2008-
dc.identifier.citationIntelligent Control and 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23. p. 2542-2547.-
dc.identifier.isbn978-1-4244-2113-8-
dc.identifier.urihttp://hdl.handle.net/1942/9557-
dc.description.abstractPID neural network (PID-NN) is a new type of dynamic feed-forward network which combines neural network with PID control strategy. It performs a perfect function in process control with the merit of both general PID controller and neural network. In this paper, the concepts of variable integral and partial differential are introduced in the design of hidden-layer of PID-NN to improve the capabilities of neurons. The structure of system identification is analyzed, and the results of simulation with field data of wet FGD indicate the validity and superiority of this improved modeling approach.-
dc.language.isoen-
dc.publisherIEEE-
dc.titleIdentification research on improved PID neural network and its application-
dc.typeProceedings Paper-
local.bibliographicCitation.conferencename7th World Congress on Intelligent Control and Automation (WCICA'08)-
local.bibliographicCitation.conferenceplaceChongqing, China, 25-27 June 2008-
dc.identifier.epage2547-
dc.identifier.spage2542-
local.bibliographicCitation.jcatC1-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
dc.bibliographicCitation.oldjcatC1-
dc.identifier.doi10.1109/WCICA.2008.4593323-
dc.identifier.isi000259965702016-
local.bibliographicCitation.btitleIntelligent Control and 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23-
item.accessRightsClosed Access-
item.fulltextNo Fulltext-
item.validationecoom 2010-
item.contributorSHEN, Yongjun-
item.contributorGu, X.-
item.contributorBao, Qiaoliang-
item.fullcitationSHEN, Yongjun; Gu, X. & Bao, Qiaoliang (2008) Identification research on improved PID neural network and its application. In: Intelligent Control and 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23. p. 2542-2547..-
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