By Enrique H Ruspini; Piero Patrone Bonissone; Witold Pedrycz
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Additional resources for Handbook of fuzzy computation
To affirm the statement ‘the apartment is beautiful’, in the conceptual frame of ordinary life, is not the same as to affirm ‘sin2x cos2x = 1’ in the conceptual frame of mathematics. The latter statement means ‘sin2x cos2 x = 1 is true for any x between 0 and 360”’; the former is just a matter of taste and fashion. The point is that the flexibility with which some predicates are applied to objects in a considered universe of discourse should be distinguished from the uncertainty linked with events, as in the case of throwing a die, of which the result is not imprecise but uncertain.
We must remark, however, that the distinctions that we are about to draw focus solely upon different interpretations of uncertain and vague information. comprehension by elimination of unnecessary detail and precision. 1 The approximate reasoning problem Classical inferential methods employ the machinery of deductive logic to determine-from information about the state of a specific system (facts) and rules linking the truth of various assertions about aspects of system state or behavior (often referred to as knowledge)-whether a particular statement is true or false.
3:7 Why Fuzzy Logic? 4 Approximate reasoning Enrique H Ruspioi and E H Mamdani Abstract See the abstract for Chapter A2. The conceptual and practical reasons leading to the introduction of the concept of fuzzy sets and the subsequent development of a broad variety of system analysis, decision-making, and reasoning methods based on that seminal notion have been the subject of considerable discussion and controversy since the publication of Zadeh's pioneering paper. Almost without exception, however, the concerns of skeptics may be easily traced to misinterpretations of the goals of fuzzy logic and of the character of its basic concepts.
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