Advantages of fuzzy logic:
· Uses linguistic variables
· Allows imprecise/contradictory inputs
· Permits fuzzy thresholds
· Reconciles conflicting objectives
· Rule base or fuzzy sets easily modified
· Relates input to output in linguistic terms, easily understood
· Allows for rapid prototyping because the system designer doesn't need to know everything about the system before starting
· Cheaper because they are easier to design
· Increased robustness
· Simplify knowledge acquisition and representation
· A few rules encompass great complexity
· Can achieve less overshoot and oscillation
· Can achieve steady state in a shorter time interval (Rao, 1995)
(http://www.scribd.com/doc/37483809/19/Disadvantages-of-Fuzzy-Logic-Controllers)
Limitations?
· Hard to develop a model from a fuzzy system
· Require more fine tuning and simulation before operational
· Have a stigma associated with the word fuzzy (at least in the Western world); engineers and most other people are used to crispness and shy away from fuzzy control and fuzzy decision making (http://www.scribd.com/doc/37483809/19/Disadvantages-of-Fuzzy-Logic-Controllers)
fuzzy expert system used to handle uncertainties.
 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
computer-based system(it contains both hard ware and software) that can process data that are incomplete or only partially correct Fuzzy logic was introduced as an artificial intelligence technique, when it was realized that normal boolean logic would not suffice. When we make intelligent decisions, we cannot limit ourselves to "true" or "false" possibilities (boolean). We have decisions like "maybe" and other shades of gray. This is what is introduced with fuzzy logic: the ability to describe degrees of truth. Example: in fuel station if you stop a fuel injecting motor at 1.55897ltrs.it can be done with the help of fuzzy logic.fuzzy has a meaning like accurate.
Fuzzy rules are linguistic IF-THEN- constructions that have the general form "IF A THEN B" where A and B are (collections of) propositions containing linguistic variables
because fuzzy wazzy was fuzzy
fuzzy graph is not a fuzzy set, but it is a fuzzy relation.
Fuzzy wuzzy had no hair, fuzzy wuzzy wasn't fuzzy was he.
Riza C. Berkan has written: 'Fuzzy systems design principles' -- subject- s -: Fuzzy systems, System design
This theorem tells, roughly, that any mathematical system can be approximated by fuzzy logic. Hopefully this page http://sipi.usc.edu/~kosko/ helps more.
Nikola K. Kasabov has written: 'Foundations of neural networks, fuzzy systems, and knowledge engineering' -- subject(s): Expert systems (Computer science), Neural networks (Computer science), Fuzzy systems, Artificial intelligence
fuzzy wuzzy had no hair... therefore he cannot be fuzzy
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.