Computational Intelligence and Environmental Planning

Zimmermann, Hans-Jürgen

doi:10.1080/01969720490451760

Cited by 5 publications

(3 citation statements)

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“…Methods that are based on the fuzzy set theory are often also called ''intelligent'' because they belong to the area of ''computational intelligence'' that was formed in 1992 by the areas of the fuzzy set theory, artificial neural nets, and genetic computation (Zimmermann 2004).…”

Section: Dynamic Intelligent Clusteringmentioning

confidence: 99%

Knowledge Management, Knowledge Discovery, and Dynamic Intelligent Data Mining

Zimmermann¹

2006

Cybernetics and Systems

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During the last two to three decades, many scientific as well business areas have moved from a situation of a lack of (electronically) readable information into a situation of abundant data. Data warehouses appeared, and the problem of extracting information from large masses of data became more and more important. Also knowledge became a very precious commodity, and its efficient use often makes the difference between success and failure. Finding useful information or patterns in raw data is known in the literature under various names, such as knowledge discovery in data bases, data mining, knowledge extraction, information discovery, information harvesting, data archaeology, etc. Many research areas, such as machine learning, pattern recognition, artificial intelligence, knowledge acquisition for expert systems, data visualization, and others are concerned with these activities, and the terminology used is not unequivocally defined.In this article, we shall first consider the different interpretations of the notions previously mentioned, and we shall then describe in more detail a recent technology that is very useful for data mining as well as for related areas.

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Section: Dynamic Intelligent Clusteringmentioning

confidence: 99%

Knowledge Management, Knowledge Discovery, and Dynamic Intelligent Data Mining

Zimmermann¹

2006

Cybernetics and Systems

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“…Fuzzy programming and/or chance-constrained programming methods were incorporated into the IMILP framework to address the interval, fuzzy and/or random information in MSW management problems. Many integrated studies were undertaken through combined uses of the CCP, FMP and/or IMILP methods [6,18,29,30,34,35,53]. For example, Tanaka et al [40] presented possibility distributions of fuzzy decision variables based on possibilistic linear programming.…”

Section: Introductionmentioning

confidence: 99%

“…Huang et al [21] developed an integrated fuzzy-stochastic linear programming method and applied it to waste management. Zimmermann [53] proposed a computational intelligence approach for supporting environmental systems optimization. This led to that the development of fuzzy application systems had to be efficient, and making fuzzy set theory into fuzzy technology.…”

Section: Introductionmentioning

confidence: 99%

Interval-parameter Fuzzy-stochastic Semi-infinite Mixed-integer Linear Programming for Waste Management under Uncertainty

Guo

Huang

et al. 2008

Environ Model Assess

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An interval-parameter fuzzy-stochastic semiinfinite mixed-integer linear programming (IFSSIP) method is developed for waste management under uncertainties. The IFSSIP method integrates the fuzzy programming, chance-constrained programming, integer programming and interval semi-infinite programming within a general optimization framework. The model is applied to a waste management system with three disposal facilities, three municipalities, and three periods. Compared with the previous methods, IFSSIP have two major advantages. One is that it can help generate solutions for the stable ranges of the decision variables and objective function value under fuzzy satisfaction degree and different levels of probability of violating constraints, which are informative and flexible for solution users to interpret/justify. The other is that IFSSIP can not only handle uncertainties through constructing fuzzy and random parameter, but also reflect dynamic features of the system conditions through interval function of time over the planning horizon. By comparing IFSSIP with interval-parameter mixed-integer linear semiinfinite programming and parametric programming, the IFSSIP method is more reasonable than others.

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Fuzzy Set and Fuzzy Logic

Leung¹

2020

International Encyclopedia of Human Geography

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Computational Intelligence and Environmental Planning

Cited by 5 publications

References 50 publications

Knowledge Management, Knowledge Discovery, and Dynamic Intelligent Data Mining

Knowledge Management, Knowledge Discovery, and Dynamic Intelligent Data Mining

Interval-parameter Fuzzy-stochastic Semi-infinite Mixed-integer Linear Programming for Waste Management under Uncertainty

Fuzzy Set and Fuzzy Logic

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