1. Artificial neural network used for
All of these
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2. A perceptron has two inputs x1 and x2 with weights w1 and w2 and a bias weight of w0. The activation function of the perceptron is h(x). The output of the perceptron is given by:
y = h(w1x1 + w2x2 + w0)
y = h(w1 + w2 + w0 )
y = w1x1 + w2x2 + w0
y = h(w1x1 + w2x2 − w0)
3. We provide a training input x to a perceptron learning rule. The desired output is t and the actual
output is o. If learning rate is η, the weight update performed by the learning rule is given by the equation?
wi ← wi + η(t − o)
wi ← wi + η(t − o)x
wi ← η(t − o)x
wi ← wi + (t − o)
4. Suppose we have n training examples xi, i = 1,.....,n, whose desired outputs are ti, i = 1,.....,n. The output of a perceptron for these training examples xi's are oi, i = 1,.....,n .The error function minimised by the gradient descend perceptron learning algorithm is:
5. Three main basic features involved in characterizing membership function are
Intuition, Inference, Rank Ordering
Fuzzy Algorithm, Neural network, Genetic Algorithm
Core, Support , Boundary
Weighted Average, center of Sums, Median
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