well, Fuzzy logic is a form of mathematical implementation in which truth can assume a continuum of values between 0 and 1 (i.e. for attaching numeric uncertainty values to rules and facts in expert systems). It divides data into vague categories such as "slightly", "quite" and "very". This enables the system to handle imprecise information. (For example: Fuzzy logic implemented in household appliances such as AC, Washing machine and in digital image processing and even in machineries such as Elevators). Fuzzy logic systems' rules resemble the following: 'if this set of criteria is satisfied, in combination, to a given degree, then take action'.

Answered by Romi , an ibibo Master, at 3:02 PM on June 27, 2008

Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem (Klir 1997).

Degrees of truth are often confused with probabilities. However, they are distinct conceptually; fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or condition. For example, if a 100-ml glass contains 30 ml of water, then, for two fuzzy sets, Empty and Full, one might define the glass as being 0.7 empty and 0.3 full. Note that the concept of emptiness would be subjective and thus would depend on the observer or designer. Another designer might equally well design a set membership function where the glass would be considered full for all values down to 50 ml. A probabilistic setting would first define a scalar variable for the fullness of the glass, and second, conditional distributions describing the probability that someone would call the glass full given a specific fullness level. Note that the conditioning can be achieved by having a specific observer that randomly selects the label for the glass, a distribution over deterministic observers, or both. While fuzzy logic avoids talking about randomness in this context, this simplification at the same time obscures what is exactly meant by the statement the 'glass is 0.3 full'.

Fuzzy logic allows for set membership values to range (inclusively) between 0 and 1, and in its linguistic form, imprecise concepts like "slightly", "quite" and "very". Specifically, it allows partial membership in a set. It is related to fuzzy sets and possibility theory. It was introduced in 1965 by Lotfi Zadeh at the University of California, Berkeley.

Fuzzy logic is controversial in some circles and is rejected by some control engineers and by most statisticians who hold that probability is the only rigorous mathematical description of uncertainty.[verification needed] Critics also argue that it cannot be a superset of ordinary set theory since membership functions are defined in terms of conventional sets
this is taken from source http://en.wikipedia.org/wiki/F uzzy_ logic

Answered by Yash , an ibibo Master, at 9:03 PM on June 24, 2008

Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem (Klir 1997).

Degrees of truth are often confused with probabilities. However, they are distinct conceptually; fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or condition. For example, if a 100-ml glass contains 30 ml of water, then, for two fuzzy sets, Empty and Full, one might define the glass as being 0.7 empty and 0.3 full. Note that the concept of emptiness would be subjective and thus would depend on the observer or designer. Another designer might equally well design a set membership function where the glass would be considered full for all values down to 50 ml. A probabilistic setting would first define a scalar variable for the fullness of the glass, and second, conditional distributions describing the probability that someone would call the glass full given a specific fullness level. Note that the conditioning can be achieved by having a specific observer that randomly selects the label for the glass, a distribution over deterministic observers, or both. While fuzzy logic avoids talking about randomness in this context, this simplification at the same time obscures what is exactly meant by the statement the 'glass is 0.3 full'.

Fuzzy logic allows for set membership values to range (inclusively) between 0 and 1, and in its linguistic form, imprecise concepts like "slightly", "quite" and "very". Specifically, it allows partial membership in a set. It is related to fuzzy sets and possibility theory. It was introduced in 1965 by Lotfi Zadeh at the University of California, Berkeley.

Fuzzy logic is controversial in some circles and is rejected by some control engineers and by most statisticians who hold that probability is the only rigorous mathematical description of uncertainty.[verification needed] Critics also argue that it cannot be a superset of ordinary set theory since membership functions are defined in terms of conventional sets
this is taken from source http://en.wikipedia.org/wiki/F uzzy_ logic

Answered by Shobhit , an ibibo Master, at 7:56 PM on June 20, 2008

hi sandeep Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem (Klir 1997).

Degrees of truth are often confused with probabilities. However, they are distinct conceptually; fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or condition. For example, if a 100-ml glass contains 30 ml of water, then, for two fuzzy sets, Empty and Full, one might define the glass as being 0.7 empty and 0.3 full. Note that the concept of emptiness would be subjective and thus would depend on the observer or designer. Another designer might equally well design a set membership function where the glass would be considered full for all values down to 50 ml. A probabilistic setting would first define a scalar variable for the fullness of the glass, and second, conditional distributions describing the probability that someone would call the glass full given a specific fullness level. Note that the conditioning can be achieved by having a specific observer that randomly selects the label for the glass, a distribution over deterministic observers, or both. While fuzzy logic avoids talking about randomness in this context, this simplification at the same time obscures what is exactly meant by the statement the 'glass is 0.3 full'.

Fuzzy logic allows for set membership values to range (inclusively) between 0 and 1, and in its linguistic form, imprecise concepts like "slightly", "quite" and "very". Specifically, it allows partial membership in a set. It is related to fuzzy sets and possibility theory. It was introduced in 1965 by Lotfi Zadeh at the University of California, Berkeley.

Fuzzy logic is controversial in some circles and is rejected by some control engineers and by most statisticians who hold that probability is the only rigorous mathematical description of uncertainty.[verification needed] Critics also argue that it cannot be a superset of ordinary set theory since membership functions are defined in terms of conventional sets
this is taken from source http://en.wikipedia.org/wiki/Fuzzy_ logic

Answered by Arti singh , an ibibo Specialist, at 1:55 PM on June 16, 2008

well, Fuzzy logic is a form of mathematical implementation in which truth can assume a continuum of values between 0 and 1 (i.e. for attaching numeric uncertainty values to rules and facts in expert systems). It divides data into vague categories such as "slightly", "quite" and "very". This enables the system to handle imprecise information. (For example: Fuzzy logic implemented in household appliances such as AC, Washing machine and in digital image processing and even in machineries such as Elevators). Fuzzy logic systems' rules resemble the following: 'if this set of criteria is satisfied, in combination, to a given degree, then take action'.

Answered by Raksha , an ibibo Master, at 9:32 AM on June 12, 2008

hi sandeep Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem (Klir 1997).

Degrees of truth are often confused with probabilities. However, they are distinct conceptually; fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or condition. For example, if a 100-ml glass contains 30 ml of water, then, for two fuzzy sets, Empty and Full, one might define the glass as being 0.7 empty and 0.3 full. Note that the concept of emptiness would be subjective and thus would depend on the observer or designer. Another designer might equally well design a set membership function where the glass would be considered full for all values down to 50 ml. A probabilistic setting would first define a scalar variable for the fullness of the glass, and second, conditional distributions describing the probability that someone would call the glass full given a specific fullness level. Note that the conditioning can be achieved by having a specific observer that randomly selects the label for the glass, a distribution over deterministic observers, or both. While fuzzy logic avoids talking about randomness in this context, this simplification at the same time obscures what is exactly meant by the statement the 'glass is 0.3 full'.

Fuzzy logic allows for set membership values to range (inclusively) between 0 and 1, and in its linguistic form, imprecise concepts like "slightly", "quite" and "very". Specifically, it allows partial membership in a set. It is related to fuzzy sets and possibility theory. It was introduced in 1965 by Lotfi Zadeh at the University of California, Berkeley.

Fuzzy logic is controversial in some circles and is rejected by some control engineers and by most statisticians who hold that probability is the only rigorous mathematical description of uncertainty.[verification needed] Critics also argue that it cannot be a superset of ordinary set theory since membership functions are defined in terms of conventional sets
this is taken from source http://en.wikipedia.org/wiki/Fuzzy_ logic

Answered by laks , an ibibo Master, at 4:30 PM on May 14, 2008

A mathematical technique for dealing with imprecise data and problems that have many solutions rather than one. Although it is implemented in digital computers which ultimately make only yes-no decisions, fuzzy logic works with ranges of values, solving problems in a way that more resembles human logic.

Fuzzy logic is used for solving problems with expert systems and real time systems that must react to an imperfect environment of highly variable, volatile or unpredictable conditions. It "smoothes the edges" so to speak, circumventing abrupt changes in operation that could result from relying on traditional either-or and all-or-nothing logic.

Answered by Nagendra , an ibibo Master, at 8:27 AM on May 13, 2008

sandeep Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem (Klir 1997).

Degrees of truth are often confused with probabilities. However, they are distinct conceptually; fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or condition. For example, if a 100-ml glass contains 30 ml of water, then, for two fuzzy sets, Empty and Full, one might define the glass as being 0.7 empty and 0.3 full. Note that the concept of emptiness would be subjective and thus would depend on the observer or designer. Another designer might equally well design a set membership function where the glass would be considered full for all values down to 50 ml. A probabilistic setting would first define a scalar variable for the fullness of the glass, and second, conditional distributions describing the probability that someone would call the glass full given a specific fullness level. Note that the conditioning can be achieved by having a specific observer that randomly selects the label for the glass, a distribution over deterministic observers, or both. While fuzzy logic avoids talking about randomness in this context, this simplification at the same time obscures what is exactly meant by the statement the 'glass is 0.3 full'.

Fuzzy logic allows for set membership values to range (inclusively) between 0 and 1, and in its linguistic form, imprecise concepts like "slightly", "quite" and "very". Specifically, it allows partial membership in a set. It is related to fuzzy sets and possibility theory. It was introduced in 1965 by Lotfi Zadeh at the University of California, Berkeley.

Fuzzy logic is controversial in some circles and is rejected by some control engineers and by most statisticians who hold that probability is the only rigorous mathematical description of uncertainty.[verification needed] Critics also argue that it cannot be a superset of ordinary set theory since membership functions are defined in terms of conventional sets.

for more inf check source http://en.wikipedia.org/wiki/F uzzy_logic

Answered by raj u , an ibibo Master, at 3:31 PM on May 08, 2008

Fuzzy logic is designed for situations where information is inexact and traditional digital on/off decisions are not possible. It divides data into vague categories such as "hot", "medium" and "cold".

Answered by pooja , an ibibo Master, at 7:09 PM on January 29, 2008

Fuzzy logic is a form of mathematical implementation in which truth can assume a continuum of values between 0 and 1 (i.e. for attaching numeric uncertainty values to rules and facts in expert systems). It divides data into vague categories such as "slightly", "quite" and "very". This enables the system to handle imprecise information. (For example: Fuzzy logic implemented in household appliances such as AC, Washing machine and in digital image processing and even in machineries such as Elevators). Fuzzy logic systems' rules resemble the following: 'if this set of criteria is satisfied, in combination, to a given degree, then take action'.

Answered by A.R.Karthick , an ibibo Master, at 8:16 PM on January 28, 2008