1. What was the main point of difference between the adaline & perceptron model?
a) weights are compared with output
b) sensory units result is compared with output
c) analog activation value is compared with output
d) all of the mentioned
Explanation: Analog activation value comparison with output,instead of desired output as in perceptron model was the main point of difference between the adaline & perceptron model
2. In adaline model what is the relation between output & activation value(x)?
a) linear
b) nonlinear
c) can be either linear or non-linear
d) none of the mentioned
Explanation: s,output=f(x)=x. Hence its a linear model
3. what is the another name of weight update rule in adaline model based on its functionality?
a) LMS error learning law
b) gradient descent algorithm
c) both LMS error & gradient descent learning law
d) none of the mentioned
Explanation: weight update rule minimizes the mean squared error(delta square), averaged over all inputs & this laws is derived using negative gradient of error surface weight space, hence option a & b.
4. In neural how can connectons between different layers be achieved?
a) interlayer
b) intralayer
c) both interlayer and intralayer
d) either interlayer or intralayer
Explanation: Connections between layers can be made to one unit to another and within the units of a layer
5. Connections across the layers in standard topologies & among the units within a layer can be organised?
a) in feedforward manner
b) in feedback manner
c) both feedforward & feedback
d) either feedforward & feedback
Explanation: Connections across the layers in standard topologies can be in feedforward manner or in feedback manner but not both.
6. What is an instar topology?
a) when input is given to layer F1, the the jth(say) unit of other layer F2 will be activated to maximum extent
b) when weight vector for connections from jth unit (say) in F2 approaches the activity pattern in F1(comprises of input vector)
c) can be either way
d) none of the mentioned
Explanation: Restatement of basic definition of instar.
7. What is an outstar topology?
a) when input is given to layer F1, the the jth(say) unit of other layer F2 will be activated to maximum extent
b) when weight vector for connections from jth unit (say) in F2 approaches the activity pattern in F1(comprises of input vector)
c) can be either way
d) none of the mentioned
Explanation: Restatement of basic definition of outstar.
8. The operation of instar can be viewed as?
a) content addressing the memory
b) memory addressing the content
c) either content addressing or memory addressing
d) both content & memory addressing
Explanation: Because in instar, when input is given to layer F1, the the jth(say) unit of other layer F2 will be activated to maximum extent
9. The operation of outstar can be viewed as?
a) content addressing the memory
b) memory addressing the content
c) either content addressing or memory addressing
d) both content & memory addressing
Explanation: Because in outstar, when weight vector for connections from jth unit (say) in F2 approaches the activity pattern in F1(comprises of input vector)
10. If two layers coincide & weights are symmetric(wij=wji), then what is that structure called?
a) instar
b) outstar
c) autoassociative memory
d) heteroassociative memory
Explanation: In autoassociative memory each unit is connected to every other unit & to itself