Applied Decision Support with Soft Computing by Marek Makowski, Andrzej P. Wierzbicki (auth.), Professor

By Marek Makowski, Andrzej P. Wierzbicki (auth.), Professor Xinghuo Yu, Professor Janusz Kacprzyk (eds.)

Soft computing has supplied refined methodologies for the improvement of clever selection aid structures. speedy advances in gentle computing applied sciences, akin to fuzzy common sense and structures, man made neural networks and evolutionary computation, have made on hand strong challenge illustration and modelling paradigms, and studying and optimisation mechanisms for addressing glossy selection making concerns. This ebook presents a finished assurance of up to date conceptual frameworks in greatly perceived selection aid platforms and profitable purposes. varied from different present books, this quantity predominately specializes in utilized determination aid with tender computing. components lined contain making plans, administration finance and management in either the non-public and public sectors.

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Both choices require a deep understanding of their consequences, which can hardly be formally assessed. Therefore they are based on a mixture of modeler's knowledge, experience and intuition. g. [46] for a more detailed discussion of related topics. 1 2 As already illustrated in Figure 1, a mathematical model is used for predicting the consequences of decisions x, which can be either proposed by a DM or computed by a DSS. The consequences are measured by values of outcome variables y. Therefore, a model can be represented by mapping y = F(x, z), where x E Ex, z E E z , and y E E y are vectors of values of decisions, external decisions, and outcomes, respectively.

G. ll A choice of variables' types is often a result of a compromise between adequacy of a problem representation and resources needed for the development of a corresponding model. Such a choice is done together with a choice of type of relations, which is discussed below. Both choices require a deep understanding of their consequences, which can hardly be formally assessed. Therefore they are based on a mixture of modeler's knowledge, experience and intuition. g. [46] for a more detailed discussion of related topics.

The latter set is not given explicitly - we usua11y only know how to compute its elements - and the issue is how to analyze the elements and properties of the Pareto ("North- 38 East") frontier of this set. This is usually called a multi-objective analysis of a model. After specifying any set of variables of a model as objectives qi, the modeIer should first know - at least approximately - the ranges in which these variables might vary. , combine them into one function (not necessarily by summation).

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Applied Decision Support with Soft Computing by Marek Makowski, Andrzej P. Wierzbicki (auth.), Professor
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