Computational Intelligence In Decision And Control Proceedings Of The 8th International Flins Conference
Download Computational Intelligence In Decision And Control Proceedings Of The 8th International Flins Conference PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Computational Intelligence In Decision And Control Proceedings Of The 8th International Flins Conference book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Computational Intelligence In Decision And Control - Proceedings Of The 8th International Flins Conference
FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the eighth edition in the series of FLINS conferences cover state-of-the-art research, development, and technology for computational intelligence systems in general, and for intelligent decision and control in particular.
Computational Intelligence: Foundations And Applications - Proceedings Of The 9th International Flins Conference
FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Computational Intelligence for applied research. The contributions to the ninth in the series of FLINS conferences cover state-of-the-art research, development, and technology for computational intelligence systems — both from foundations and applications points-of-view.
COMPUTATIONAL INTELLIGENCE IN COMPLEX DECISION MAKING SYSTEMS
Author: Ruan Da
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-06-01
In recent years, there has been a growing interest in the need for designing intelligent systems to address complex decision systems. One of the most challenging issues for the intelligent system is to effectively handle real-world uncertainties that cannot be eliminated. These uncertainties include various types of information that are incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, deficient, and overloading. The uncertainties result in a lack of the full and precise knowledge of the decision system, including the determining and selection of evaluation criteria, alternatives, weights, assignment scores, and the final integrated decision result. Computational intelligent techniques (including fuzzy logic, neural networks, and genetic algorithms etc.), which are complimentary to the existing traditional techniques, have shown great potential to solve these demanding, real-world decision problems that exist in uncertain and unpredictable environments. These technologies have formed the foundation for intelligent systems.