TY - JOUR
T1 - Computational Intelligence in Nuclear Applications: Lessons Learned and Recent Developments
AU - Ruan, Da
AU - Hines, Wesley
AU - Pazsit, Imre
A2 - Van den Eynde, Gert
N1 - Score = 10
PY - 2005
Y1 - 2005
N2 - The main objective of this special issue is to publish a peer-reviewed collection of high quality papers in the relevant topic areas. The focus is on those papers that provide theoretical/analytical solutions to the problems of real interest in computational intelligence for nuclear systems (e.g., uncertainties in measurements). In addition, most contributions have shown a sound conclusion that constitutes added value and technical limitations by applying computational intelligence in nuclear systems. It is hoped that the special issue will provide a clear picture of some recommendations on the future use of computational intelligence in nuclear systems.
The Special Issue considers papers addressing the use of computational intelligence for the design, analysis, optimization, control, learning, data/sensor/information fusion, etc., for nuclear systems. With a rigorous review process, we have selected 16 papers from FLINS 2004 related to computational intelligence in nuclear applications with more or less four groups. The first group (four papers respectively by Hines and Uhrig; Hines and Davis; Roverso; Fantoni) reviewed trends in computational intelligence in nuclear engineering, lessons learned from the U.S. NPP on-line monitoring programs, and the OECD Halden Reactor Project's experiences. The second group (five papers respectively by Zhao and Upadhyaya; Mori et al.; Figedy and Oksa; Lee and Seong; Zio and Baraldi) presented applications of computational intelligence in NPPs. The third group (four papers respectively by Domingos et al.; Benitez et al.; Benitez et al.; Adda et al.) reported recent progress on nuclear reactor control with fuzzy and soft computing techniques. The last group (three papers respectively by Sunde et al.; Garcia et al.; Fiordaliso and Kunsch) showed some potential uses of new combined computational intelligence methods to classification, data analysis and decision support systems in nuclear applications.
AB - The main objective of this special issue is to publish a peer-reviewed collection of high quality papers in the relevant topic areas. The focus is on those papers that provide theoretical/analytical solutions to the problems of real interest in computational intelligence for nuclear systems (e.g., uncertainties in measurements). In addition, most contributions have shown a sound conclusion that constitutes added value and technical limitations by applying computational intelligence in nuclear systems. It is hoped that the special issue will provide a clear picture of some recommendations on the future use of computational intelligence in nuclear systems.
The Special Issue considers papers addressing the use of computational intelligence for the design, analysis, optimization, control, learning, data/sensor/information fusion, etc., for nuclear systems. With a rigorous review process, we have selected 16 papers from FLINS 2004 related to computational intelligence in nuclear applications with more or less four groups. The first group (four papers respectively by Hines and Uhrig; Hines and Davis; Roverso; Fantoni) reviewed trends in computational intelligence in nuclear engineering, lessons learned from the U.S. NPP on-line monitoring programs, and the OECD Halden Reactor Project's experiences. The second group (five papers respectively by Zhao and Upadhyaya; Mori et al.; Figedy and Oksa; Lee and Seong; Zio and Baraldi) presented applications of computational intelligence in NPPs. The third group (four papers respectively by Domingos et al.; Benitez et al.; Benitez et al.; Adda et al.) reported recent progress on nuclear reactor control with fuzzy and soft computing techniques. The last group (three papers respectively by Sunde et al.; Garcia et al.; Fiordaliso and Kunsch) showed some potential uses of new combined computational intelligence methods to classification, data analysis and decision support systems in nuclear applications.
KW - computational intelligence
KW - nuclear science
KW - nuclear engineering
KW - FLINS
UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_27283
UR - http://knowledgecentre.sckcen.be/so2/bibref/2737
U2 - 10.1016/j.pnucene.2005.03.001
DO - 10.1016/j.pnucene.2005.03.001
M3 - Article
VL - 46
SP - 165
EP - 387
JO - Progress in Nuclear Energy
JF - Progress in Nuclear Energy
IS - 3-4
ER -