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Titolo:
Dynamic recurrent neural networks for a hybrid intelligent decision support system for the metallurgical industry
Autore:
Zhou, SM; Xu, LD;
Indirizzi:
Chinese Acad Sci, China Remote Sensing Satellite Ground Stn, Beijing 100086, Peoples R China Chinese Acad Sci Beijing Peoples R China 100086 100086, Peoples R China Wright State Univ, Dept MSIS, Dayton, OH 45435 USA Wright State Univ Dayton OH USA 45435 iv, Dept MSIS, Dayton, OH 45435 USA
Titolo Testata:
EXPERT SYSTEMS
fascicolo: 4, volume: 16, anno: 1999,
pagine: 240 - 247
SICI:
0266-4720(199911)16:4<240:DRNNFA>2.0.ZU;2-H
Fonte:
ISI
Lingua:
ENG
Soggetto:
TIME;
Keywords:
hybrid intelligent system; dynamic recurrent neural networks; real-time systems; manufacturing;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
23
Recensione:
Indirizzi per estratti:
Indirizzo: Zhou, SM Chinese Acad Sci, China Remote Sensing Satellite Ground Stn, POB 2434, Beijing 100086, Peoples R China Chinese Acad Sci POB 2434 Beijing Peoples R China 100086 R China
Citazione:
S.M. Zhou e L.D. Xu, "Dynamic recurrent neural networks for a hybrid intelligent decision support system for the metallurgical industry", EXPERT SYS, 16(4), 1999, pp. 240-247

Abstract

Knowledge-based modeling and implementation of the various manufacturing processes represent an intensive research area. It is known that it is difficult to analyze the mechanisms of many industrial production processes and build dynamic models by employing classical methods for intelligent systemsin manufacturing. This paper describes how to use dynamic recurrent neuralnetworks to provide the model base of a hybrid intelligent system for the metallurgical industry with a quality control model. The hybrid system extracts the features of image sequences obtained through the vision detection subsystem and employs a dynamic recurrent neural network to assess and predict the product qualities to further coordinate the entire production process.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 03/04/20 alle ore 09:38:44