Introduction:
This
is the first article intended to share information and knowledge in
the field of Fuzzy
Logic ( FL ) and its application. The main motivation of this
article will is to make you familiar with the term or concept in the
form of Question like Where did it come from (History) ? What is
fuzzy logic and How it works?
The
entire question mentioned above will be solved out in a systematic
and interesting manner with the aim to journalize the term ‘Fuzzy
Logic’ in front of all the readers.
Where
did Fuzzy logic come from? Or History of Fuzzy Logic?
The
concept of Fuzzy Logic (FL) was conceived by Lotfi Zadeh, 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. Professor Zadeh reasoned that people
do not require precise, numerical information input, and yet they
are capable of highly adaptive control. If feedback controllers
could be programmed to accept noisy, imprecise input, they would be
much more effective and perhaps easier to implement. Unfortunately,
U.S. manufacturers have not been so quick to embrace this technology
while the Europeans and Japanese have been aggressively building
real products around it.
What
is Fuzzy Logic?
FL
is a problem solving controlled in the system range from simple,
small and embedded micro-controller to large, networked Pc’s and
control system. The implementation of FL is very convenient and
simple and it can be implemented in Hardware, Software or in both.
The
Basic Intention of Fl provides a simple way to obtain a definite and
accurate solution which is based upon, imprecise, unclear, noisy, or
missing input information. FL’s approach to control problem is
very much similar that how a person would make a decision, but in
must faster way.
How
Does FL work ?
FL
needs some numerical data/parameters in order to handle some error
which are considered significant error & significant
rate-of-change of error, but exact values are not critical until a
very responsive performance is required in which they can be
determine by empirical tuning.
For
example, a simple temperature control system could use a single
temperature feedback sensor whose data is subtracted from the
command signal to compute "error" and then
time-differentiated to yield the error slope or
rate-of-change-of-error, hereafter called "error-dot".
Error might have units of degs F and a small error considered to be
2F while a large error is 5F. The "error-dot" might then
have units of degs/min with a small error-dot being 5F/min and a
large one being 15F/min. These values don't have to be symmetrical
and can be "tweaked" once the system is operating in order
to optimize performance. Generally, FL is so forgiving that the
system will probably work the first time without any tweaking.
SUMMARY
FL
was conceived as a better method for sorting and handling data but
has proven to be a excellent choice for many control system
applications since it mimics human control logic. It can be built
into anything from small, hand-held products to large computerized
process control systems. It uses an imprecise but very descriptive
language to deal with input data more like a human operator. It is
very robust and forgiving of operator and data input and often works
when first implemented with little or no tuning.
Ref
: http://www.seattlerobotics.org/encoder/mar98/fuz/fl_part1.html#INTRODUCTION
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