Fuzzy Reasoning in Decision Making and OptimizationPhysica, 27/08/2012 - 338 páginas Many decision-making tasks are too complex to be understood quantitatively, however, humans succeed by using knowledge that is imprecise rather than precise. Fuzzy logic resembles human reasoning in its use of imprecise informa tion to generate decisions. Unlike classical logic which requires a deep under standing of a system, exact equations, and precise numeric values, fuzzy logic incorporates an alternative way of thinking, which allows modeling complex systems using a higher level of abstraction originating from our knowledge and experience. Fuzzy logic allows expressing this knowledge with subjective concepts such as very big and a long time which are mapped into exact numeric ranges. Since knowledge can be expressed in a more natural by using fuzzy sets, many decision (and engineering) problems can be greatly simplified. Fuzzy logic provides an inference morphology that enables approximate human reasoning capabilities to be applied to knowledge-based systems. The theory of fuzzy logic provides a mathematical strength to capture the un certainties associated with human cognitive processes, such as thinking and reasoning. The conventional approaches to knowledge representation lack the means for representating the meaning of fuzzy concepts. As a consequence, the approaches based on first order logic do not provide an appropriate con ceptual framework for dealing with the representation of commonsense knowl edge, since such knowledge is by its nature both lexically imprecise and non categorical. |
Índice
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Fuzzy Reasoning | 100 |
Fuzzy Reasoning for Fuzzy Optimization | 156 |
Applications in Management | 207 |
Future Trends in Fuzzy Reasoning and Decision Making | 254 |
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Outras edições - Ver tudo
Fuzzy Reasoning in Decision Making and Optimization Christer Carlsson,Robert Fuller Pré-visualização limitada - 2001 |
Fuzzy Reasoning in Decision Making and Optimization Christer Carlsson,Robert Fuller Pré-visualização indisponível - 2014 |
Fuzzy Reasoning in Decision Making and Optimization Christer Carlsson,Robert Fuller Pré-visualização indisponível - 2010 |
Palavras e frases frequentes
a₁ aggregation operator applications benchmarks bullwhip effect compositional rule computed crisp criteria data sources data warehouse decision models defined degree denoted DSS database elements equation example extension principle FLP problem forecasting fuzzy implication operator fuzzy logic fuzzy quantities fuzzy reasoning Fuzzy Sets fuzzy solution fuzzy subsets hyperknowledge i-th implication operator inequality relation input IntA interdependences large numbers law of large Lemma linear programming linguistic variables MAX(x membership function model-based scenarios monotonicity notation objective function optimal value otherwise output OW scenarios OWA operator Pos[A Pos[Z possibilistic possibilistic mean possibility distribution programming problem quantifier ratings real number real option rule of inference Sets and Systems small changes software agents strategic supply chain support system symmetric triangular fuzzy t-conorm t-norm triangular fuzzy numbers triangular norms w₁ weighted aggregate Yager Zadeh