Fuzzy Reasoning in Decision Making and OptimizationSpringer Science & Business Media, 09/10/2001 - 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. |
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Outras edições - Ver tudo
Fuzzy Reasoning in Decision Making and Optimization Christer Carlsson,Robert Fuller Pré-visualização limitada - 2012 |
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 applications approximate reasoning benchmarks bullwhip effect Carlsson cognitive maps collaborative agents compositional rule constraints context crisp criteria data sources data warehouse decision models decision problem defined denoted DSS database elements extension principle FLP problem forecasting Fullér fuzzy logic fuzzy quantities fuzzy reasoning Fuzzy Sets fuzzy solution fuzzy subsets fuzzy system hyperknowledge i-th impact implication operator inequality relation input IntA interaction interdependences Internet interpretation agent law of large Lemma linear programming MAX(x maximizing solution membership function model-based scenarios multiobjective multiple objective function optimal value otherwise output OW scenarios OWA operator Pos[Z possibilistic possibility distribution programming problem real number real option rule of inference ScA1 Sets and Systems software agents supply chain support system symmetric triangular fuzzy t-norm Theorem triangular fuzzy numbers triangular norms vector w₁ weighted aggregate Zadeh