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Neural Network Practice Test: Feedforward Neural Networks
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Feedforward Neural Networks topics include: Pattern association, pattern classification, weight determination, pattern mapping and storage analysis and the technique of backpropagation algorithm. A feedforward neural network is a type of artificial neural network where the nodes' connections do not form a loop. Information flows in a forward manner only, from the input nodes, through the hidden nodes (if any), and to the output nodes.  Feedforward neural networks are also known as multi-layered networks of neurons (MLN). The simplest type of feedforward neural network is the perceptron,... Show more
Neural Network Practice Test: Feedforward Neural Networks
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25 Questions

1. If a(l) gives output b(l) & a’=a(l)+m,where m is small quantity & if a’ gives ouput b(l) then?
2. Feedforward networks are used for?
3. What are the features that can be accomplished using affine transformations?
4. The hard learning problem is ultimately solved by hoff’s algorithm?
5. When line joining any two points in the set lies entirely in region enclosed by the set in M-dimensional space , then the set is known as?
6. If the output produces nonconvex regions, then how many layered neural is required at minimum?
7. The perceptron convergence theorem is applicable for what kind of data?
8. What are 3 basic types of neural nets that form basic functional units among
i)feedforward ii) loop iii) recurrent iv) feedback v) combination of feed forward & back
9. The number of units in hidden layers depends on?
10. If a(l) gives output b(l) & a’=a(l)+m,where m is small quantity & if a’ gives ouput b(l)+n then?
11. In perceptron learning, what happens when input vector is correctly classified?
12. When two classes can be separated by a separate line, they are known as?
13. Can system be both interpolative & accretive at same time?
14. What are hard problems?
15. w(m + 1) = w(m) + n(b(m) - s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight, can this model be used for perceptron learning?
16. Intersection of linear hyperplanes in three layer network can only produce convex surfaces, is the statement true?
17. The simplest combination network is called competitive learning network?
18. Feedback networks are used for?
19. In a linear autoassociative network, if input is noisy than output will be noisy?
20. As dimensionality of input vector increases, what happens to linear separability?
21. In determination of weights by learning, for orthogonal input vectors what kind of learning should be employed?
22. Two classes are said to be inseparable when?
23. Is it true that percentage of linearly separable functions will increase rapidly as dimension of input pattern space is increased?
24. If e(m) denotes error for correction of weight then what is formula for error in perceptron learning model: w(m + 1) = w(m) + n(b(m) - s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight
25. In determination of weights by learning, for linear input vectors what kind of learning should be employed?

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