Cognitive Techniques MCQs

Welcome to our comprehensive collection of Multiple Choice Questions (MCQs) on Cognitive Techniques, a fundamental topic in the field of Cognitive Radio. Whether you're preparing for competitive exams, honing your problem-solving skills, or simply looking to enhance your abilities in this field, our Cognitive Techniques MCQs are designed to help you grasp the core concepts and excel in solving problems.

In this section, you'll find a wide range of Cognitive Techniques mcq questions that explore various aspects of Cognitive Techniques problems. Each MCQ is crafted to challenge your understanding of Cognitive Techniques principles, enabling you to refine your problem-solving techniques. Whether you're a student aiming to ace Cognitive Radio tests, a job seeker preparing for interviews, or someone simply interested in sharpening their skills, our Cognitive Techniques MCQs are your pathway to success in mastering this essential Cognitive Radio topic.

Note: Each of the following question comes with multiple answer choices. Select the most appropriate option and test your understanding of Cognitive Techniques. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

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Cognitive Techniques MCQs | Page 6 of 7

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Q51.
In ____ time, Markov chain is called as ____

a.

continuous, Markov process

b.

discrete, Markov process

c.

continuous, Bayesian process

d.

discrete, Bayesian process

Discuss
Answer: (a).continuous, Markov process Explanation:A Markov chain is a stochastic model. It represents a progression of events in which the probability of each event depends on the state achieved by the past event.
Q52.
What does the term “hidden” refer to in hidden Markov model technique?

a.

Parameters of input

b.

Sate sequence

c.

Parameters of model

d.

Fixed standards of model

Discuss
Answer: (b).Sate sequence Explanation:In a hidden Markov model, the state is not directly available to the observer. The output, dependant on the state, is available. The parameters of the model are not hidden, and the model is referred to as hidden Markov model even when the parameters are explicitly available.
Q53.
Which among the following is not commonly determined by using hidden Markov model?

a.

Channel transition probabilities

b.

Decision making in cellular network

c.

Channel quality

d.

Signal classification

Discuss
Answer: (d).Signal classification Explanation:Hidden Markov models use past data to predict future actions. It may be used to describe channel quality. It determines whether the process should move to the next or remain in the present state. The current channel statistics may be modelled and use for decision making in cellular network.
Q54.
A hidden Markov model can be considered a generalisation of mixture model.

a.

True

b.

False

c.

May be True or False

d.

Can't say

Discuss
Answer: (a).True Explanation:A mixture model is used to represent the presence of a sub-category within an overall category. In hidden Markov model, the hidden variables are related based on Markov process and are not independent of each other. The hidden variables control the mixture component to be selected for each observation.
Q55.
Which among the following gives the definition of fuzzy logic?

a.

Fuzzy logic follows Boolean logic where zero stands for true and one stands for false

b.

Fuzzy logic follows Boolean logic where zero stands for false and one stands for true

c.

Fuzzy logic allows the assignment of any real number from zero to one to variables

d.

Fuzzy logic allows the assignment of any whole number from zero to one to variables

Discuss
Answer: (c).Fuzzy logic allows the assignment of any real number from zero to one to variables Explanation:Fuzzy logic allows the assignment of any real number from zero to one to variables. It is a multi-valued logic. It implements the condition of partial truth. The truth value may fall anywhere between completely false to completely true.
Q56.
Which among the following is a false statement about Fuzzy logic?

a.

Fuzzy logic may use imprecise information

b.

Fuzzy logic uses only accurate information

c.

Fuzzy logic may represent vague information

d.

Fuzzy logic may use non-numerical information

Discuss
Answer: (b).Fuzzy logic uses only accurate information Explanation:Fuzzy logic is based on the observation that decision making sometimes involves making use of inaccurate and imprecise information. The fuzzy logic models are generally vague but are capable of identifying, representing, and modifying unclear information and data.
Q57.
Which among the following is the correct sequence of events that occur in application of fuzzy logic?

a.

Fuzzify the input, execute the applicable rules, and de-fuzzify the output

b.

Execute the applicable rules, fuzzify the input, and de-fuzzify the output

c.

Fuzzify the input, de-fuzzify the output, and execute the applicable rules

d.

Execute the applicable rules, fuzzify the input, and fuzzify the output

Discuss
Answer: (a).Fuzzify the input, execute the applicable rules, and de-fuzzify the output Explanation:The implementation of fuzzy logic generally involves three steps namely fuzzify the input into fuzzy membership functions, execute every rule in the defined rule base to generate the fuzzy output, de-fuzzify the output to get distinct output values.
Q58.
What is the term used to refer to variables that take non-numerical as input?

a.

String variables

b.

Character variables

c.

Linguistic variables

d.

Morphological variables

Discuss
Answer: (c).Linguistic variables Explanation:Linguistic variables are assigned non-numerical values to aid the representation of facts and rules. For example, a linguistic variable may take “hot” and “cold” as input. In order to take advantage of the scale offered by fuzzy logic, additional words such as “rather” or “somewhat” may be used.
Q59.
Which among the following uses past solutions to solve current problems?

a.

Artificial neural networks

b.

Fuzzy logic

c.

Knowledge based systems

d.

Case based reasoning

Discuss
Answer: (d).Case based reasoning Explanation:Case based reasoning is the process of developing solutions to current problems based on solutions that have already been applied under similar conditions. The process initially involves the comparison of functional and operational characteristics for resemblance. If the similarity is limited, then reasoning is applied for modification of solution to suit the current problem.
Q60.
Which among the following is an algorithm useful for case based reasoning?

a.

Viterbi algorithm

b.

K- nearest neighbours algorithm

c.

Forward algorithm

d.

Backward algorithm

Discuss
Answer: (b).K- nearest neighbours algorithm Explanation:K-nearest neighbours algorithm uses k number of training examples that have input characteristics similar to that of current condition under interest to fix target value for the current condition. This may be accomplished by using mode, average, or some other interpolation technique.
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