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Fuzzy Logic

Fuzzy Logic is a term used to identify a new trend of quantifying partial truths. One disadvantage of most rule sets that they cannot process inconsistent data. Fuzzy logic is a superset of conventional logic that has been extended to handle the concept of partial truth, being values that lie between "completely true" and "completely untrue". Dr. Lotfi Zadeth of UC/Berkley first introduced it in the 1960s as a means of modeling the uncertainty of natural language. All this works by using expanding on Boolean logic and the concept of subsets. In classical set theory, a subset X of a set Y can be defined as a mapping from the elements of Y to the elements of the set (0,1), X: Y --* (0,1). This mapping can be represented by a set of ordered pairs, with one ordered pair present for each element of the set X, and the second element is an element of the set (0,1). The value 0 represents non-membership, and the value 1 is used to represent membership. This approach is limited to only two opposing possible outcomes. If a variable z is in X, then the statement is true if z=1 and false if z=0. Fuzzy logic takes this process a few steps further and quantifies the degree of membership instead of stopping at a positive or negative correlation.


A fuzzy subset F of a set Y can be defined as a set of ordered pairs, each with the first element from S, and the second element from the interval [0,1], with exactly one ordered pair present for each element of S. This defines a mapping between elements of the set S and values in the interval [0,1]. The value zero is still used to represent complete non-membership, the value one is used to represent complete membership, and values in between are used to represent intermediate degrees of membership. The set S is referred to as the universe of discourse for the fuzzy subset F. Frequently, the mapping is described as a function, the membership function of F. The degree to which the statement x is in F is true is determined by finding the ordered pair whose first element is x. The degree of truth of the statement is the second element of the ordered pair. To understand this graphically, lets use a Zadeh example that measures tallness. In this case the set S (the universe of discourse) is the set of people. We define a fuzzy subset TALL, which will answer the question "to what degree is person x tall?" Zadeh describes TALL as a linguistic variable, which represents our cognitive category of "tallness". To each person in the universe of discourse, we have to assign a degree of membership in the fuzzy subset TALL. The easiest way to do this is with a membership function based on the person's height. The following figure is how to define this using a graph. Fig. 1 tall(x) = { 0, if height(x) * 5 ft., (height(x)-5ft.)/2ft., if 5 ft. *= height (x) *= 7 ft., 1, if height(x) * 7 ft. } A graph of this looks like: 1.0 + +------------------- | / | / 0.5 + / | / | / 0.0 +-------------+-----+------------------- | | 5.0 7.0 height, ft. -* Person Height degree of tallness -------------------------------------- Billy 3' 2" 0.00 Yoke 5' 5" 0.21 Drew 5' 9" 0.38 Erik 5' 10" 0.42 Mark 6' 1" 0.54 Kareem 7' 2" 1.00 The application of fuzzy logic allows us to go beyond the simple categories of "tall" and "not tall" and apply a real world understanding of other outside variables that are always included when considering humanistic characteristics. If someone were to ask "how tall is Shaq?", you probably wouldn't say "tall", you would probably add a descriptive variable like "really tall". Fuzzy logic is a way of measuring more discrete variables for further understanding. Fuzzy logic is used directly in very few applications. One good example of its application is the Sony Palmtop. This device is one of the new handheld messaging systems that have become very popular of late. With it, you can use a computer light pen to write words on the screen. The computer then uses a form of fuzzy

Some topics in this essay:
Zadeth UC/Berkley, Fuzzy CLIPS, Person Height, Bernard Improved, LINE Feb, Sony Palmtop, Professor Yen's, Final Project, Fuzzy Logic, Improvement Recommendation, fuzzy logic, fuzzy subset, | /, gathered information, / | /, application fuzzy, information milestone, universe discourse, milestone 1, milestone 2, milestone 1 feb, gathered information milestone, pair element, application fuzzy logic, understanding fuzzy logic,

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Approximate Word count = 1805
Approximate Pages = 7 (250 words per page double spaced)


  

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