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Contrast intensification hedges (eg extremely), where memberships lower than 1/2 are diminished, but memberships larger than 1/2 are elevated This hedge is de ned as, A (x) = which intensi es contrast Vague hedges (eg seldom), are opposite to contrast intensi cation hedges, having membership values altered using A (x) = A (x)/2 if A (x) 05 1 (1 A (x))/2 if A (x) > 05 (214) 2p 1 A (x)p 1 2p 1 (1 A (x))p if A (x) 05 if A (x) > 05 (213)

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ENG Electronic news-gathering; portable video production for the news Often quick-and-dirty techniques are used with minimal equipment and crew equalization A tone adjustment for audio frequencies, often needed to boost high or low tones coming from a phonograph cartridge, microphone, or audiotape head external bus A bus that connects a computer to peripheral devices Two examples are the Universal Serial Bus (USB) and IEEE-1394 fade out Make a TV picture smoothly grow black

Vague hedges introduce more fuzziness into the set Probabilistic hedges, which express probabilities, eg likely, not very likely, probably, etc

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For fuzzy systems in general, the dynamic behavior of that system is characterized by a set of linguistic fuzzy rules These rules are based on the knowledge and experience of a human expert within that domain Fuzzy rules are of the general form if antecedent(s) then consequent(s) (215)

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The antecedent and consequent of a fuzzy rule are propositions containing linguistic variables In general, a fuzzy rule is expressed as if A is a and B is b then C is c (216)

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where A and B are fuzzy sets with universe of discourse X1 , and C is a fuzzy set with universe of discourse X2 Therefore, the antecedent of a rule form a combination of fuzzy sets through application of the logic operators (ie complement, intersection, union) The consequent part of a rule is usually a single fuzzy set, with a corresponding membership function Multiple fuzzy sets can also occur within the consequent, in which case they are combined using the logic operators

Figure 211 Fuzzy Rule-Based Reasoning System Together, the fuzzy sets and fuzzy rules form the knowledge base of a fuzzy rule-based reasoning system In addition to the knowledge base, a fuzzy reasoning system consists of three other components, each performing a speci c task in the reasoning process, ie fuzzi cation, inferencing and defuzzi cation The di erent components of a fuzzy rule based system are illustrated in Figure 211 The question is now what sense can be made from a single fuzzy rule For example, for the rule, if Age is Old the Speed is Slow (217) what can be said about Speed if Age has the value of 70 Given the membership functions for Age and Speed in Figure 212(a), then nd Old (70), which is 04 For linguistic variable Speed nd the intersection of the horizontal line with membership function Slow This gives the shaded area in Figure 212(b) A defuzzi cation operator (refer to Section 212) is then used to nd the center of gravity of the shaded area, which gives the value of Speed = 3 This simple example leads to the next question: How is a plausible outcome determined if a system has more than one rule This question is answered in the next section

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