Back to Question 1.
Answer: B. Fuzzy logic follows the principle of Aristotle and Buddha.
Explanation: Aristotle and Buddha followed principles which contradicted each other.
Aristotle stated that a something is true or not true, whereas Buddha
stated that something can be true and also not true to a certain extent.
Aristotle followed the concept of bivalence and Buddha followed the concept of multivalence.
Why the others are wrong?
A.Fuzzy logic is a concept of `certain degree'.
Fuzzy logic differs to Boolean logic in a sense that something can be true to a
certain extent and does not have to be just true or false.
C.Japan is currently the most active users of fuzzy logic.
When it was founded in the 60s the Americans and the rest of the world totally ignored the idea. Instead,
it was adopted by the Japanese followed by Korea and other parts of the East. Currently
70% of Japanese products use fuzzy logic.
D. Boolean logic is a subset of fuzzy logic.
Since Boolean logic only holds for values of 1 or 0 and fuzzy logic holds for
a range of values from 1 to 0.
Q2. Where is the minimum criterion used ?
Answer: A. When there is an AND operation .
Why the others are wrong?
B. When there is an OR operation.
Q3. Considering a graphical representation of the `tallness' of people using
its appropriate member function, which of the following combibnations are
true ?
Answer B. i & iii
Q4. What is the Fuzzy Appoximation Theorem(FAT)?
Answer: A. A fuzzy sytem can model any continous sytem.
Why the others are wrong
B. The conversion of fuzzy logic to probability.
Q5. What is the main difference between probability and fuzzy logic
Answer: C. Probability is ADDITIVE, meaning all its values must add up to one.
Why the others are wrong.
A. Fuzzy logic is probability in disguise.
Q6 In an adaptive fuzzy system
Answer: B. i & ii
Q7 What are the following sequence of steps taken in designing a fuzzy logic
machine?
Answer A:Fuzzification->Rule evaluation->Defuzzification
Q8. Fuzzy logic has rapidly become one of the most successful of today's
technologies for developing sophisticated control systems. The reason for
this is:
Explanation:
Q9
Answer: A
Explanation: The minimum criterion takes the least value in the set given. In an AND operation
all the values have to be true in order for the whole expression to be true. Hence taking
the least of the values ensures that the range of all values are met.
In an OR operation any value in the set can be true in order for the whole operation
to be true. In this case the maximum criterion is used
C. In De Morgan's Theorem.
This is clearly wrong as any of the criterions have nothing to do with De Morgan's theorem.
D. None of the above.
Since A has already been justified as correct, this is clearly wrong as well.
i. TALL is usually the fuzzy subset.
ii. HEIGHT is usually the fuzzy set.
iii. PEOPLE is usually the universe of discourse.
When doing a graphical representation of fuzzy subsets, it is important to
the definitions clear before the actual drawing.
i. TALL is usually the fuzzy subset.
ii. HEIGHT is usually the actual membership function in which we define.
iii. The variable `people' is usually
the universe of discourse because it covers the whole subset.
Hence the answer is i & iii.
Explanation: The Fuzzy Approximation Theorem(FAT) as stated by Bart
Kosko shows a fuzzy sytem can model
any contionous system. Each of the rules acts as a fuzzy patch that the system
places so as to resemble the response of the continous system.
Fuzzy logic and probability are two completely different concepts and cannot
be converted to one another.
C. A continous sytem can model any fuzzy system.
This is vice-versa of the answer and is clearly wrong.
D. Fuzzy patches covering a series of fuzzy rules.
The fuzzy rules are represented by fuzzy patches, and the patches are suppose
to be able to cover any curve in a continous system.
Eplanation: This is the main difference between fuzzy logic and probability. Although,
both probability and fuzzy logic contain values between the ranges 1 and 0, fuzzy
logic tells the extent of a specific member function, whereas probability gives
the frequency, hence all values of its set must add up to one.
This is an assumtion that pro-probability scientists usually make. It is clearly
untrue or this question would not be asked.
B. Fuzzy logic is the likelihood of an event occuring and probablility is the extent of that event.
Quite the contrary. Probablity is the likelihood of an event occuring and
fuzzy logic is the extent of that event occuring.
D. Probability dissipates with decreasing information.
Probability dissipates, in fact with increasing information. This means that
the more information we have, the more likely the probability of that event occuring.
i. The machine learns as more data are fed into it.
ii. Nueral network is used to find the fuzzy rules.
iii. The system creates rules without the intevention of human beings.
Explanation:
i. An adaptive fuzzy machine works using the DIRO(Data In Rules Out) principle.
This means that as data is fed into a black box, rules are generated. The black
box is a nuearal network and is beyond the scope of the two articles.
ii. As said in i. , nueral networks are used to generate the fuzzy rules, and is
beyond thescope of this article.
iii. This is a bit ambigous as we are led to believe that the main concept of an
adaptive fuzzy sytem is that machines should be able to think for themselves. However,
it is still the user(human being) that feeds in data, hence there still is human intervention.
Hence the answer is B. i & ii
Explanation: When designing a fuzzy logic, we first have to define the fuzzy sets, and
make appropriate member functions. Then rule evaluation comes in which matches the
sets to its corresponding rules(a series of if-then statements). More about this
can be found in the fuzzy rules section on Shahariz Aziz's second article by clicking
here .
Why other's are wrong?
The other's are simply rearrangements of the correct answer and are obviously wrong.
i. Fuzzy logic mimics the human way of thinking.
ii. Fuzzy logic enables the ability to generate precise solutions
from certain or approximate information.
iii. Fuzzy logic is easy to implement.
Answer: B. i & ii
i. Fuzzy logic applies the concept of `certain degree' which is similar to the
way human beings think. Instead of just being either true or false, fuzzy logic
can be true partially and also false partially at the same time. This is similar to
the human mind.
ii. Fuzzy logic can uses exact points representing to what degree an event occurs,
and with fuzzy rules, generate precise outcomes.
iii. This is not true because a fuzzy machine with just 5 fuzzy rules, take
weeks to design and months or even years to simulate and work properly. It is
relatively easy to think of, but difficult to implement.
Please refer to Parthiban's first article for a more thorough explanation by clicking
here
Explanation: Since this is an OR operation, we use the maximum criterion
in which the maximum value of the set is taken.
Please click here to refer
to the fuzzy logic operations section in Shahariz Aziz's article#1.