What is Fuzzy Logic?
A
computational paradigm that is based on how humans think Fuzzy Logic looks at
the world in imprecise terms, in much the same way that our brain takes in
information (e.g. temperature is hot, speed is slow), then responds with precise
actions.
The
human brain can reason with uncertainties, vagueness, and judgments. Computers
can only manipulate precise valuations. Fuzzy logic is an attempt to combine
the two techniques. “Fuzzy” – a misnomer, has resulted in the mistaken suspicion
that FL is somehow less exacting than traditional logic
History of fuzzy logic
In 1965,
Lotfi A. Zadeh of the University of California at Berkeley published
"Fuzzy
Sets," which laid out the mathematics of fuzzy set theory and, by extension,
fuzzy logic. Zadeh had observed that conventional computer logic couldn't
manipulate data that represented subjective or vague ideas, so he created fuzzy
logic to allow computers to determine the distinctions among data with shades
of gray, similar to the process of human reasoning.
20 years later after its
conception
Interest
in fuzzy systems was sparked by Seiji Yasunobu and Soji Miyamoto of Hitachi,
who in 1985 provided simulations that demonstrated the superiority of fuzzy
control systems for the Sendai railway. Their ideas were adopted, and fuzzy
systems were used to control accelerating and braking when the line opened in 1987.
Also
in 1987, during an international meeting of fuzzy researchers in Tokyo, Takeshi
Yamakawa demonstrated the use of fuzzy control, through a set of simple
dedicated fuzzy logic chips, in an "inverted pendulum" experiment.
This is a classic control problem, in which vehicle tries to keep a pole
mounted on its top by a hinge upright by moving back and forth.
Observers
were impressed with this demonstration, as well as later experiments by Yamakawa
in which he mounted a wine glass containing water or even a live mouse to the
top of the pendulum. The system maintained stability in both cases. Yamakawa eventually
went on to organize his own fuzzy-systems research lab
to
help exploit his patents in the field.
Introduction of Fuzzy Logic in
Engineering World
Fuzzy
Logic is one of the most talked-about technologies to hit the embedded control
field in recent years. It has already transformed many product markets in Japan
and Korea, and has begun to attract a widespread following. In the United
States. Industry watchers predict that fuzzy technology is on its way to
becoming a multibillion-dollar business.
Fuzzy
Logic enables low cost microcontrollers to perform functions traditionally
performed
by more powerful expensive machines enabling lower cost products
to
execute advanced features.
What is artificial intelligence?
“The
study and design of intelligent agents, where an intelligent agent is a system
that perceives its environment and takes actions that maximize its chances of
success.”
Application 0f Fuzzy Logic to
Artificial Intelligence
In the city of Sendai in
Japan, a 16-station subway system is controlled by a fuzzy computer (Seiji
Yasunobu and Soji Miyamoto of Hitachi) – the
ride is so smooth, riders do not need to hold
straps.
Nissan – fuzzy automatic transmission, fuzzy anti-skid
braking system
CSK, Hitachi – Hand-writing Recognition
Sony - Hand-printed character recognition
Ricoh, Hitachi – Voice recognition
Tokyo’s stock market has had at least one stock-trading portfolio
based on Fuzzy Logic that outperformed the
Nikkei exchange average.
NASA has studied fuzzy control for automated space docking: simulations show that a fuzzy control system
can greatly reduce fuel consumption.
Canon developed an auto-focusing camera that
uses a charge-coupled device (CCD) to measure the clarity of the image in six
regions of its field of view and use the information provided to determine if
the image is in focus. It also tracks the rate of change of lens movement
during focusing, and controls its speed to prevent overshoot. The camera's
fuzzy control system uses 12 inputs: 6 to obtain the current
clarity data provided by the CCD and 6 to measure the rate of change of lens
movement. The output is the position of the lens. The fuzzy control system uses 13 rules and requires
1.1 kilobytes of memory.
For washing machines,
Fuzzy Logic control is almost becoming a standard feature Others: Samsung, Toshiba, National, Matsushita,
etc. fuzzy controllers to load-weight, fabric-mix, and
dirt sensors and automatically set the wash cycle for
the best use of power, water, and detergent.
What is control
system?
This is a device which produce a set of desired outputs
for a given set of inputs.
For example:
A household thermostat takes a temperature input
and sends a control
signal to a furnace.
A car engine controller responds to variables
such as engine position, manifold pressure and cylinder temperature to regulate
fuel flow and spark timing.
Conventional Control
Vs Fuzzy
Look up table
In
the simplest case, a controller takes its cues from a look-up table, which
tells
what
output to produce for every input or combination of inputs.
Sample
The
table might tell the controller,
“IF temperature
is 85, THEN increase furnace fan speed to 300 RPM.”
Drawback
The
problem with the tabular approach is that the table can get very long, especially
in situations where there are many inputs or outputs. And that, in turn, may
require more memory than the controller can handle, or more than is
cost-effective. Tabular control mechanisms may also give a bumpy, uneven response,
as the controller jumps from one table-based value to the next.
Mathematical Formula
The
usual alternative to look-up tables is to have the controller execute a mathematical
formula – a set of control equations that express the output
as a
function of the input. Ideally, these equations represent an accurate model of
the system behaviour. The formulas can be very complex, and working them out in
real-time may be more than an affordable controller (or machine) can manage.
Downside of Mathematical
Modelling
It
may be difficult or impossible to derive a workable mathematical model in the
first
place, making both tabular and formula-based methods impractical. Though an
automotive engineer might understand the general relationship between say,
ignition timing, air flow, fuel mix and engine RPM, the exact math that
underlies those interactions may be completely obscure.
Why use Fuzzy logic?
FL
overcomes the disadvantages of both table-based and formula-based control.
Fuzzy
has no unwieldy memory requirements of look-up tables, and no heavy
number-crunching demands of formula-based solutions.
FL can make development and
implementation much simpler. It needs no intricate mathematical models, only a
practical understanding of the overall system behaviour.
FL
mechanisms can result to higher accuracy and smoother control as well. FL differs from orthodox logic in that it is
multivalued. Fuzzy deals with
degrees of truth and degrees of membership.
NOTE: Fuzzy logic is one of the add-in of Matlab. It means
that if you have Matlab installed on you PC then you automatically have access
to Fuzzy logic interface.
For training and help regarding your project,
feel free to contact abataysoftwarewizard Research Institute.
08130582034
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