Q. Back propagation is a learning technique that adjusts weights in the neural network by propagating weight changes.

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Q. Identify the following activation function :
φ(V) = Z + (1/ 1 + exp (– x * V + Y) ),
Z, X, Y are parameters 

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Q. An artificial neuron receives n inputs x1, x2, x3............xn with  weights w1, w2, ..........wn attached to the input links. The weighted sum_________________ is computed to be passed on to a non-linear filter  Φ called activation function to release the output.

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Q. Match the following knowledge representation techniques with their applications:

List – I List – II

(a) Frames (i) Pictorial representation of objects, their attributes and relationships

(b) Conceptual dependencies (ii) To describe real world stereotype events

(c) Associative networks (iii) Record like structures for grouping closely related knowledge

(d) Scripts (iv) Structures and primitives to represent sentences

code:
a b c d

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Q. In propositional logic P ⇔ Q is equivalent to (Where ~ denotes NOT):

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Q. Slots and facets are used in

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Q. A neuron with 3 inputs has the weight vector [0.2 -0.1 0.1]^T and a bias θ = 0. If the input vector is X = [0.2 0.4 0.2]^T then the total input to the neuron is:

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Q. Which of the following neural networks uses supervised learning?

(A) Multilayer perceptron
(B) Self organizing feature map
(C) Hopfield network

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Q. Consider the following statements:

(a) If primal (dual) problem has a finite optimal solution, then its dual (primal) problem has a finite optimal solution.
(b) If primal (dual) problem has an unbounded optimum solution, then its dual (primal) has no feasible solution at all.
(c) Both primal and dual problems may be infeasible.
Which of the following is correct?

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