Classical theory is a reference to established theory. Fuzzy set theory is a reference to theories that are not widely accepted.
The fundamental difference is that in fuzzy set theory permits the gradual assessment of the membership of elements in a set and this is described with the aid of a membership function valued in the real unit interval [0, 1]. Better, the degree of membership of the elements of a set can take values ranging between 0 and 1 allowing for a ranking of membership. Conversely, crisp set theory is a classical bivalent set so that the membership function only takes values 0 or 1. In this case, one can know only if an element of the set have or not a particular characteristic and a ranking of membership is not possible.
The difference between probability and fuzzy logic is clear when we consider the underlying concept that each attempts to model. Probability is concerned with the undecidability in the outcome of clearly defined and randomly occurring events, while fuzzy logic is concerned with the ambiguity or undecidability inherent in the description of the event itself. Fuzziness is often expressed as ambiguity rather than imprecision or uncertainty and remains a characteristic of perception as well as concept.
Crisp :Binary logicIt may be occur or non occurindicator functionFuzzy logicContinuous valued logicmembership functionConsider about degree of membership
Certainly fuzzy logic is not the best in solving uncertainty, but..... it is on of the best alternatives to that exists to model uncertainty.
ans is Fuzziest.
The fundamental difference is that in fuzzy set theory permits the gradual assessment of the membership of elements in a set and this is described with the aid of a membership function valued in the real unit interval [0, 1]. Better, the degree of membership of the elements of a set can take values ranging between 0 and 1 allowing for a ranking of membership. Conversely, crisp set theory is a classical bivalent set so that the membership function only takes values 0 or 1. In this case, one can know only if an element of the set have or not a particular characteristic and a ranking of membership is not possible.
= http://en.wikipedia.org/wiki/Fuzzy_set = = Fuzzy set =Jump to: navigation, searchFuzzy sets are sets whose elements have degrees of membership. Fuzzy sets have been introduced by Lotfi A. Zadeh (1965) as an extension of the classical notion of set. In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition - an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function valued in the real unit interval [0, 1]. Fuzzy sets generalize classical sets, since the indicator functions of classical sets are special cases of the membership functions of fuzzy sets, if the latter only take values 0 or 1.
Fuzzy and pinky are both types of textures. Fuzzy typically refers to something that is soft and has a slightly rough or uneven surface, like a fuzzy blanket or a fuzzy peach. Pinky, on the other hand, usually refers to something that is a shade of pink in color, like a pinky swear or pinky finger. So, the main difference between fuzzy and pinky is that fuzzy describes texture, while pinky describes color.
 Fuzzy inference is a computer paradigm based on fuzzy set theory, fuzzy if-then- rules and fuzzy reasoning  Applications: data classification, decision analysis, expert systems, times series predictions, robotics & pattern recognition  Different names; fuzzy rule-based system, fuzzy model, fuzzy associative memory, fuzzy logic controller & fuzzy system Fuzzy inference is a computer paradigm based on fuzzy set theory, fuzzy if-then- rules and fuzzy reasoning  Applications: data classification, decision analysis, expert systems, times series predictions, robotics & pattern recognition  Different names; fuzzy rule-based system, fuzzy model, fuzzy associative memory, fuzzy logic controller & fuzzy system
fuzzy differential equation (FDEs) taken account the information about the behavior of a dynamical system which is uncertainty in order to obtain a more realistic and flexible model. So, we have r as the fuzzy number in the equation whereas ordinary differential equations do not have the fuzzy number.
the difference is that a dandelion is yellow MOSTLY and a rose is MOSTLY red Jahzy :)
Each crisp number is a single point.example 3 or 5.5 or6.But each fuzzy number is a fuzzy set with different degree of closeness to a given crisp number example,about 3,nearly 5 and a half,almost 6.
The distinction is a little fuzzy, but generally everything before Quantum and Relativistic Physics is considered classical. The conceptual distinctions are numerous, but in general the label 'modern' carries a meaning of loss of certainty- Relativity did away with the ceratinty of a perfect frame of reference, and Quantum mechanics let go of determinism and instead embraced a random, statistical model of particle behaviour.
Valerie Cross has written: 'Similarity and compatibility in fuzzy set theory' -- subject(s): Fuzzy sets
The difference between probability and fuzzy logic is clear when we consider the underlying concept that each attempts to model. Probability is concerned with the undecidability in the outcome of clearly defined and randomly occurring events, while fuzzy logic is concerned with the ambiguity or undecidability inherent in the description of the event itself. Fuzziness is often expressed as ambiguity rather than imprecision or uncertainty and remains a characteristic of perception as well as concept.
The Big Bang Theory - 2007 The Fuzzy Boots Corollary 1-3 is rated/received certificates of: Argentina:13
It would probably be an algorithm using fuzzy logic.Traditional logic has only two possible outcomes, true or false. Fuzzy logic instead uses a graded scale with many intermediate values, like a number between 0.0 and 1.0. (Similar to what probability theory does.)A fuzzy algorithm would then use fuzzy logic to operate on inputs and give a result. Applications include control logic (controlling engine speed, for instance, where it can be handy to have some intermediate values between "full speed" and "full stop") and edge detection in images.See related link.