On the application of artificial intelligence techniques to the quality improvement of industrial processes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2308, Issue , 2002, Pages 473-484

  Georgilakis, Pavlos ^a   Hatziargyriou, Nikos ^b  

^a Elvim Plant   (Greece)

^b NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS;

ARTIFICIAL INTELLIGENCE TECHNIQUES; INDUSTRIAL PROCESSS; IRON LOSS; NEURAL NETWORK METHOD; PROCESS PARAMETERS; QUALITY IMPROVEMENT; TRANSFORMER MANUFACTURING;

DECISION TREES;

EID: 33745806456     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-46014-4_42     Document Type: Conference Paper

References (6)

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2. Wehenkel, L., Pavella, M.: Decision trees and transient stability of electric power systems. Automatica, 27 (1), 1991, 115-134
3. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. In Rumelhart, D.E., McClelland, J.L., (eds.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations, Cambridge, MA: M.I.T. Press, 1986
4. Wehenkel, L.: Automatic Learning Techniques in Power Systems. Kluwer Academic (1998)
5. Hatziargyriou, N.D., Georgilakis, P.S., Spiliopoulos, D.S., Bakopoulos, J.A.: Quality improvement of individual cores of distribution transformers using decision trees. Int. Journal of Engineering Intelligent Systems, 6 (3), September 1998, 29-34
6. AI Ware “NNET 210 User’s Manual”, AI Ware Incorporated, Cleveland, OH, 1989