Fuzzy logic Sample Clauses

Fuzzy logic. Fuzzy logic was introduced by Xxxxx Xxxxx in 1965. The method concentrates on solving specific problems rather than trying to model a whole system. It is a multi-valued logic, which introduces fuzzy sets in addition to crisp sets. In crisp sets elements either belong to a set or not. Fuzzy sets allow elements to belong to a set to a certain degree. Therefore fuzzy logic allows one to integrate imprecise approximations and semantic notions such as high / medium / low into mathematical formulations and computer models. The first step in a fuzzy logic system is called fuzzyfication: for each input parameter a degree of membership is assigned for linguistic values by the membership function of the fuzzy set. Based on expert knowledge the second step connects the fuzzy set with logic rules for the linguistic values. Also preconditions of the fuzzy sets have to be applied to the linguistic values depending on the actions of the logic rules. This is called inference. In most cases there is more than one logic rule applied to linguistic values in rule systems. Therefore membership functions of the actions have to be combined after inference building into a generic membership function. This is called composition. Table 4.1 shows the logic rules for the example of SAR remote sensing data by Xxxxxxxx (2001). The last step in a fuzzy rule system is called defuzzyfication. It derives an output parameter from the generic membership function Xxxx (2000). Figure 4.1 shows an example by Xxxxxxxx (2001) for the classification of SAR remote Sensing data.
Sample 1
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Fuzzy logic. In classical set theory the basic operators conjunction, disjunction and com- plement are defined using Boolean logic. For example, the (classical) set of people who are both young and tall can be defined by the following member- ship function: ⎩
Sample 1

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