Allow for fractions partial data imprecise data Fuzzify the data you have How red is this 1 RGB value 150255 What Is a Fuzzy Controller What Is a Fuzzy Controller Simply put it is fuzzy code designed to control something usually mechanical They ca ID: 26689 Download Pdf

By Andrew Pro. References. Alexander, Thor, “An Optimized Fuzzy Logic Architecture For Decision Making”, . AI Game Wisdom. .. Bonissone. , P. . Piero. and . Cheetham. , William, “Fuzzy Case Based Reasoning For Residential Property Valuation” (.

by: Ashley Reynolds. Where Fuzzy Logic Falls in the Field of Mathematics . Mathematics. Mathematical Logic and Foundations. Fuzzy Logic. Computer Science. Logic in Artificial Intelligence. Reasoning Under Uncertainty .

Fuzzy Logic. Lotfi. . Zadeh. (professor at UC Berkeley) wrote his original paper on . fuzzy set theory. . In various occasions, this is what he said…. “Fuzzy logic is a means of presenting problems to computers in a way akin to the way humans solve them”.

Frank Costanzo – MAT 7670 Spring 2012. Introduction. Fuzzy logic began with the introduction of Fuzzy Set Theory by . Lotfi. . Zadeh. in 1965.. Fuzzy Set. Sets whose elements have degrees of membership.

Project #3: Collaborative Learning using Fuzzy Logic (CLIFF) Sophia Mitchell, Pre-Junior, Aerospace Engineering ACCEND College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH

Sophia Mitchell, Pre-Junior, Aerospace Engineering ACCEND. College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH. Dr. Kelly Cohen, School of Aerospace Systems. An Extension of Fuzzy Collaborative Robotic Pong (FLIP).

Basil Hamed. Electrical . Engineering . Islamic University of Gaza. Outline. Introduction, Definitions and . Concepts. Control. Intelligent . Control. History of Fuzzy . Logic. Fuzzy Logic. Fuzzy Control.

. OF DC LINK PI CONTROL IN A DSTATCOM. Harish . Suryanarayana. . Doctoral Student. Energy Sources and Systems. Purdue University. OverVIEW. OF THE PRESENTATION. Custom Power. The Distribution Static Compensator.

March . 30. , 2014. Most of the sides are from the . Matlab. tutorial.. 1. Introduction. Fuzzy logi. c is due to the 1965 paper by Prof. . Lofti. A. . Zadeh. .. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. .

Basil Hamed. Electrical . Engineering . Islamic University of Gaza. Outline. Introduction, Definitions and . Concepts. Control. Intelligent . Control. History of Fuzzy . Logic. Fuzzy Logic. Fuzzy Control.

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Allow for fractions partial data imprecise data Fuzzify the data you have How red is this 1 RGB value 150255 What Is a Fuzzy Controller What Is a Fuzzy Controller Simply put it is fuzzy code designed to control something usually mechanical They ca

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Scott LancasterFuzzy Flight Description of Fuzzy LogicWhat Fuzzy Logic Controllers Are Used forHow Fuzzy Controllers WorkController Examplesby Scott Lancaster Fuzzy Logic by Fuzzy Logic by Lotfi ZadehLotfi ZadehProfessor at University of CaliforniaFirst proposed in 1965 as a way to process imprecise dataIts usefulness was not seen until more powerful computers and controllers were available Scott LancasterFuzzy Flight Basic Concept of Fuzzy LogicBasic Concept of Fuzzy LogicZadeh Attempt to mimic human control logicDo away with crisp sets, Boolean, true/false, etc.Allow for fractions, partial data, imprecise Fuzzifythe data you haveHow red is this? ? ? 1?RGB value 150/255 What Is a Fuzzy Controller?What Is a Fuzzy Controller?Simply put, it is fuzzy code designed to control something, usually They can be in software or hardware and can be used in anything from small circuits to large mainframes. Scott LancasterFuzzy Flight Currently Used Fuzzy ControllersCurrently Used Fuzzy Controllers F-117 Flight Control System Camcorder -Stabilization Lifting BodiesLifting Bodies http://iridia.ulb.ac.be/~famimo/ Scott LancasterFuzzy Flight Why Should We Use Fuzzy Why Should We Use Fuzzy Very robustCan be easily modifiedCan use multiple inputs and outputs Much simpler than its predecessors (linear algebraic equations)Very quick and cheaper to implement Constructing a Fuzzy ControllerConstructing a Fuzzy ControllerCreate the membership values (fuzzify).Specify the rule table.Determine your procedure for defuzzifying the result. Scott LancasterFuzzy Flight Create the Membership ValueCreate the Membership ValueFirst we have to fuzzify the data or create membership values for the data and put them into fuzzy sets.Put simply, we have to divide each set of The Y value will always be on a range of 0 to 1 (theoretically 0 to 100%).The X will be an arbitrary range that we determine Membership for Inverted Membership for Inverted Typically a fuzzy controller has at least 2 inputs and one output.For the inverted pendulum experiment, we will have angle and angular velocity as our inputs and speed as our output (the activity we want to control).The ranges you determine for each set of data can drastically determine how well the controller works. Scott LancasterFuzzy Flight Pendulum SetsPendulum SetsInputsShahariz Specify the Rule TableSpecify the Rule TableThe rule table must now be created to determine which output ranges are used.The table is an intersection of the two inputs. AngleAngular Velocity Scott LancasterFuzzy Flight If angle is Z and angular velocity is Z then speed is ZIf angle is Z and angular velocity is NH then speed is NHIf angle is Z and angular velocity is NL then speed is NLIf angle is Z and angular velocity is PL then speed is PLIf angle is Z and angular velocity is PH then speed is PHIf angle is NH and angular velocity is Z then speed is NHIf angle is NL and angular velocity is Z then speed is NLIf angle is PL and angular velocity is Z then speed is PLIf angle is PH and angular velocity is Z then speed is PHIf angle is NL and angular velocity is PL then speed is ZIf angle is PL and angular velocity is NL then speed is Z Defuzzify Defuzzify the Resultthe ResultNow we have to figure out what to do with the result we get from the rules and the fuzzy sets.The typical way is to defuzzify using Mamdanis Center of Gravity method. Scott LancasterFuzzy Flight MamdanisMamdanisMandanis prinicipal takes the input values (angle and angular velocity) and finds where they intersect their sets.The intersection creates a cuts-off line known as the alpha-cut.We fire our rules to find the corresponding output rule.The rule is then cut off by the alpha-cut, giving us several trapazoidal shapes.These shapes are added together to find their total center of gravity. Wierman Implementing Pendulum Implementing Pendulum For our first input we get our values for angle and angular velocity. These values intersect the fuzzy sets a certain points which are our Shahariz Scott LancasterFuzzy Flight Now we have to fire our rules to see which ones we will use.If angle is Z and angular velocity is Z then speed is ZIf angle is Z and angular velocity is NL then speed is NLIf angle is PL and angular velocity is Z then speed is PLIf angle is PL and angular velocity is NL then speed is Z Combine AlphaCombine Alphacut and Rulecut and RuleShahariz Scott LancasterFuzzy Flight Resulting Fuzzy SetsResulting Fuzzy Sets Shahariz Apply COG EquationApply COG Equation Scott LancasterFuzzy Flight ExamplesExamplesOnline pendulum java scripthttp://www.aptronix.com/fuzzynet/java/pend/pendjava .htm program TheEndEnd Scott LancasterFuzzy Flight ShaharizAbdulAziz, Fussy Logic and its Uses Mark Wierman, Applied Fuzzy Set Theory , Creighton

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