1. The network of figure 1, is: 1. The network of figure 1, is: 
Figure 1
2. A 3-input neuron is trained to output a zero when the input is 110 and a one when the input is 111. After generalisation, the output
will be zero when and only when the input is: 3. A perceptron is: 4. An autoassociative network is: 5. A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2.
The inputs are 4, 10, 5 and 20 respectively. The output will be: 6. Which of the following is true? 7. Which of the following is true for neural networks? 8. What are the advantages of neural networks over conventional computers? 9.Which of the following is true? 10. The network shown in Figure 1 is trained to recognize the characters H and T as shown below:
If the following pattern was given
What would be the output of the network?
| (a) |  |
| (b) |  |
| (c) |  |
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1. The answer is (a).
The network of figure 1, is a single layer feed-forward neural network because there is only one neuron between any input and
output. The network is not autoassociative i.e. doesn't have feedback because there are no loops in it.
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2. The answer is (c).
The truth table before generalisation is:
| Inputs | Output |
| 000 | $ |
| 001 | $ |
| 010 | $ |
| 011 | $ |
| 100 | $ |
| 101 | $ |
| 110 | 0 |
| 111 | 1 |
where $ represents don't know cases and the output is random.
After generalisation, the truth table becomes:
| Inputs | Output |
| 000 | 0 |
| 001 | 1 |
| 010 | 0 |
| 011 | 1 |
| 100 | 0 |
| 101 | 1 |
| 110 | 0 |
| 111 | 1 |
Therefore, the output will be zero when the input is 000 or 010 or 110 or 100
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3. The answer is (a).
The perceptron is a single layer feed-forward neural network. It is not an autoassociative network because it has no feedback
and is not a multiple layer neural network because the preprocessing stage is not made of neurons.
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4. The answer is (b).
An autoassociative network is equivalnet to a neural network that conatins feedback. The number of feedback paths(loops) does
not have to be one.
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5. The answer is (b).
The output is found by multipling the weights with their respective inputs, summing the results and multipling with the trasfer
function. Therefore:
Output = 2 * (1*4 + 2*10 + 3*5 + 4*20) = 238
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6. The answer is (a)
Neural networks have higher computational rates than conventional computers because a lot of the operation is done in parallel.
Note: that is not the case when the neural network is simulated on a computer. The idea behind neural nets is based on the way
the human brain works. Neural nets cannot be programmed, they cam only learn by examples.
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7. The answer is (c).
The training time depends on the size of the network; the number of neuron is greater and therefore the the number of possible
'states' is increased. Neural networks can be simulated on a conventional computer but the main advantage of neural networks -
parallel execution - is lost. Artificial neurons are not identical in operation to the biological ones. We don't know yet what the real
neurons do in detail.
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8. The answer is (c).
Neural networks learn by example.
They are more fault tolerant because they are always able to respond and small changes in input do not normally cause a change
in output.
Because of their parallel architecture, high computational rates are achived.
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9. The answer is (a).
Pattern recognition is what single layer neural networks are best at but they don't have the ability to find the parity of a picture or
to determine whether two shapes are connected or not.
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10. The answer is (b).
The top square of the output is black because the top pattern differs in two squares from a T and in 3 squares from an H.
The middle square is not defined because the middle row of the input defers the same amount from both T and H(differs in 1).
Therefore, the output can be either black or white.
The bottom square is black because it differs from a T and in 2 from an H.
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References:
1. Prof Aleksander books and articles.