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Manufacturing Intelligence for Industrial Engineering
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Methods for System Self-Organization, Learning, and Adaptation
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Artikel-Nr:
9781605668642
Veröffentl:
2010
Einband:
HC gerader Rücken kaschiert
Erscheinungsdatum:
21.12.2010
Seiten:
406
Autor:
Zude Zhou
Gewicht:
1279 g
Format:
286x221x26 mm
Sprache:
Englisch
Beschreibung:
Huaiqing Wang is a Professor at the Department of Information Systems, City University of Hong Kong. He is also the Honorary Dean and a Guest Professor of the School of Information Engineering, Wuhan University of Technology, China. He received his PhD in Computer Science from University of Manchester, UK, in 1987. Dr. Wang specializes in research and development of business intelligence systems, intelligent agents and their applications (such as multi-agent supported financial information systems, virtual learning systems, knowledge management systems, conceptual modeling and ontology). He has published more than 40 international refereed journal articles.
The manufacturing industry has experienced dramatic change over the years with growing advancements, implementations, and applications in technology. Manufacturing Intelligence for Industrial Engineering: Methods for System Self-Organization, Learning, and Adaptation focuses on the latest innovations for developing, describing, integrating, sharing, and processing intelligent activities in the process of manufacturing in engineering. Containing research from leading international experts, this publication provides readers with scientific foundations, theories, and key technologies of manufacturing intelligence.
Artificial neural networks Communication and interaction protocol Computing intelligence Cooperation, negotiation, and behavior coordination Industrial engineering Intelligent activities in manufacturing Intelligent agents Intelligent technology applications in manufacturing Knowledge acquisition and management Methods of data fusion Modern manufacturing Multi-sensor information fusion Problem solving and interaction mechanism Theories of multi-agent systems