Post on 19-Nov-2014
FUZZY LOGIC T.C.KanishAssistant Professor (Sr.)VIT University
OVERVIEWWhat is Fuzzy Logic?
Where did it begin?What is MatLab Fuzzy Logic Toolbox For?Fuzzy Logic in Control SystemsOverview: Fuzzy Inference Systems
Fuzzy Set ConceptFuzzy RulesMembership functionsHow it works
Building Systems: An ExampleDemoDiscussion
WHAT IS FUZZY LOGIC?
Definition of fuzzy
Fuzzy – “not clear, distinct, or precise; blurred”
Definition of fuzzy logic
A form of knowledge representation suitable for
notions that cannot be defined precisely, but which
depend upon their contexts.
FUZZY LOGIC
“As complexity rises, precise statements lose meaning and meaningful statements lose
precision”
- Lotfi Zadeh
FUZZY LOGIC COME FROM
Concept of Fuzzy Logic (FL) was conceived by LotfiZadeh, a professor at the University of California at Berkley, and presented not as a control methodology,
But as a way of processing data by allowing partial set membership rather than crisp set membership or non-membership
This approach to set theory was not applied to control systems until the 70's due to insufficient small-computer capability prior to that time.
ORIGINS OF FUZZY LOGICTraces back to Ancient Greece
Lotfi Asker Zadeh ( 1965 )
First to publish ideas of fuzzy logic.
Professor Toshire Terano ( 1972 )
Organized the world's first working group on fuzzy
systems.
F.L. Smidth & Co. ( 1980 )
First to market fuzzy expert systems.
FUZZY LOGICFL is a problem-solving control system methodology that lends itself to implementation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-channel PC or workstation-based data acquisition and control systems. It can be implemented in hardware, software, or a combination of both.FL provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. FL's approach to control problems mimics how a person would make decisions, only much faster.
FUZZY LOGIC (Cont..)Fuzzy logic provides a method to formalize reasoning when dealing with vague terms. Traditional computing requires finite precision which is not always possible in real world scenarios. Not every decision is either true or false, or as with Boolean logic either 0 or 1. Fuzzy logic allows for membership functions, or degrees of truthfulness and falsehoods. or as with Boolean logic,
not only 0 and 1 but all the numbers that fall in between.
TRADITIONAL REPRESENTATION OF LOGIC
Slow FastSpeed = 1Speed = 0
bool speed; get the speed if ( speed == 0) {
// speed is slow} else {
// speed is fast}
FUZZY LOGIC REPRESENTATIONSlowest
For every problem must represent in terms of fuzzy sets.
[ 0.0 – 0.25 ]
Slow[ 0.25 – 0.50 ]
Fast[ 0.50 – 0.75 ]
Fastest[ 0.75 – 1.00 ]
FUZZY LOGIC REPRESENTATION CONT.
Slowest Fastestfloat speed; get the speed if ((speed >= 0.0)&&(speed < 0.25)) {
// speed is slowest} else if ((speed >= 0.25)&&(speed < 0.5)) {
// speed is slow}else if ((speed >= 0.5)&&(speed < 0.75)) {
// speed is fast}else // speed >= 0.75 && speed < 1.0 {
// speed is fastest}
Slow Fast
FUZZY MATHEMATICS
Fuzzy Numbers – almost 5, or more than 50
Fuzzy Geometry – Almost Straight Lines
Fuzzy Algebra – Not quite a parabola
Fuzzy Calculus
Fuzzy Graphs – based on fuzzy points
FUZZY LOGIC VS. NEURAL NETWORKS
How does a Neural Network work?
Both model the human brain.
Fuzzy Logic
Neural Networks
Both used to create behavioral
systems.
FUZZY OPERATIONS
A B
A ∧ B A ∨ B ¬A
CONTROLLER STRUCTURE
FuzzificationScales and maps input variables to fuzzy sets
Inference MechanismApproximate reasoningDeduces the control action
DefuzzificationConvert fuzzy output values to control signals
SIMPLE FUZZY CONTROLLER
FUZZY LOGIC IN CONTROL SYSTEMS
Fuzzy Logic provides a more efficient and
resourceful way to solve Control Systems.
Some Examples
Temperature Controller
Anti – Lock Break System ( ABS )
SIMPLE TEMPARTURE CONTROL
Fuzzy based Temperature controller
RULE BASE
Air TemperatureSet cold {50, 0, 0}Set cool {65, 55, 45}Set just right {70, 65, 60}Set warm {85, 75, 65}Set hot {∞, 90, 80}
Fan Speed
o Set stop {0, 0, 0}o Set slow {50, 30, 10}o Set medium {60, 50, 40}o Set fast {90, 70, 50}o Set blast {∞, 100, 80}
RULES
Air Conditioning Controller Example:
IF Cold then StopIf Cool then SlowIf OK then MediumIf Warm then FastIF Hot then Blast
FUZZY AIR CONDITIONER
Stop
Slow
Medium
Fast
B
last
0
10
20
30
40
50
60
70
80
90
100
0
1
45 50 55 60 65 70 75 80
0
Cold
Cool
85 90
Just
Rig
ht
W
arm
Hot
if Coldthen Stop
IF CoolthenSlow
If Just Rightthen
Medium
If WarmthenFast
If HotthenBlast
MAPPING INPUTS TO OUTPUTS1
Stop
Slow
Medium
Fast
B
last
0
10
20
30
40
50
60
70
80
90
100
0
1
45 50 55 60 65 70 75 80
0
Cold
Cool
85 90
Just
Rig
ht
W
arm
Hot
t
TEMPERATURE CONTROLLERThe problem
Change the speed of a heater fan, based off the room temperature and humidity.
A temperature control system has four settingsCold, Cool, Warm, and Hot
Humidity can be defined by:Low, Medium, and High
Using this we can define the fuzzy set.
BENEFITS OF USING FUZZY LOGIC
ANTI LOCK BREAK SYSTEM ( ABS )Nonlinear and dynamic in natureInputs for Intel Fuzzy ABS are derived from
Brake4 WDFeedbackWheel speedIgnition
Outputs PulsewidthError lamp
FUZZY LOGIC IN OTHER FIELDS
Business
Hybrid Modeling
Expert Systems
FUZZY LOGIC USING MATLAB
PRIMARY GUI Tools
We can use five primary GUI tools for building, editing, and observing fuzzy inference systems in the toolbox
Fuzzy Inference System (FIS) EditorMembership Function EditorRule EditorRule ViewerSurface Viewer
PRIMARY GUI TOOLS
User Interface Layout: Getting Started
User Interface Layout: FIS Editor
UI Layout: MF Editor - Service
UI LAYOUT: MF EDITOR -FOOD
UI Layout: MF Editor - Tip
User Interface Layout: Rule Editor
User Interface Layout: Rule Viewer
User Interface Layout: Surface Viewer
CONCLUSION
Fuzzy logic provides an alternative way to
represent linguistic and subjective attributes of
the real world in computing.
It is able to be applied to control systems and
other applications in order to improve the
efficiency and simplicity of the design process.