Mindmap
COMPARATIVE STUDY OF DATA IN SHARPER AND FUZZY STRUCTURES DATA
Inhalt der Mindmap
COMPARATIVE STUDY OF DATA IN SHARPER AND FUZZY STRUCTURES DATA
intensive data structures for storing,
- to manipulate and organize information they work
- logical connection between data
- data
- are considered as raw material
- are added and combined in various ways
data structures
- is a way of organizing a set of elementary data
- is any collection or data organized such that have associated a set of operations to manipulate them.
datatypes
- static types
- means that the size stored in memory can not vary
- dynamic types
- allows greater control over memory management in your programs
- simple Types
- They are the simplest and the easiest to learn
- static types
fuzzy logic
- A fuzzy proposition p is a statement whose truth degree I is a real number between 0 and 1, denoted by the expression g (p)
- finite logical
- example
- • the irrational number
- • the sequence of rational numbers are approximate values of π
- the order of approximation required
- example
crisp sets theory
- preclude the proper treatment of this information due to imprecision or uncertainty present in the data
- example
- Crisp and fuzzy values are identified in respect of height and quality attributes. In what follows, we will indicate how the data represented in Fuzzy Relational Database (BDRD) are found, and also indicate how the data held in this vagueness or uncertainty are stored
- For example, the value $ [30, 35, 40, 45] is stored [30, 35-30, 40 - 45, 45], that is, [1, 2 ° 1 ° 3 ° 4 °, 4th], as established by Keystone Distribution.
- Crisp and fuzzy values are identified in respect of height and quality attributes. In what follows, we will indicate how the data represented in Fuzzy Relational Database (BDRD) are found, and also indicate how the data held in this vagueness or uncertainty are stored
fuzzy set theory
- defined the concept of fuzzy sets, based on the idea that there sets in which is not clearly determined whether or not an item belongs to the whole
- Given a fuzzy predicate P Unit: X → U and let (U, g) standard logic, where the set U is the universe of discourse of the fuzzy propositions and propositional variable real function g: U → [0, 1 ] denotes its truth-function
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- Mindmap am COMPARATIVE STUDY OF DATA IN SHARPER AND FUZZY STRUCTURES DATA, erstellt von alisonlilian_12 am 17/05/2014.
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- alisonlilian_12
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