Write any four activation function used in neural networks. Also, draw the graph of the output of these functions.
Activation functions play a crucial role in artificial neural networks by introducing non-linearity into the model. They help in determining the output of a neuron and are vital for the network to learn complex relationships. Here are four commonly used activation functions in neural networks along __________ _______ _____ _________ ___ __________ _______.
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Write a formula describing the function defined by a one-hidden-layer (already trained) MLP with a single output. Also, write the formula describing the function defined by a RBFN with a single output. How do they differ?
Consider the ADALINE filter with three neurons in the input layer having weights w11 = 3, w12 = -2 and w13 -2 and the input sequence {- - - , 0, 0, 0, -4, 5, 0, 0, 0 - - - - } What is the filter output?
What is competitive learning? How does it differ from Hebbian learning?
Write any three terminating conditions used in learning of a neural network.
Find the length and order of the following schema:
i) S1= (1 * * 0 0 * 1 * *)
ii) S2 = (* 0 0 * 1 * *)
iii) S3 = (* * * 1 * * *)
Consider the problem of finding the shortest route through several cities, such that each city is visited only once and in the end traveler returns to the starting city (the travelling salesman problem). Suppose that in order to solve this problem we use a genetic algorithm, in which genes represent links between pairs of cities. For example, a link between Delhi and Mumbai is represented by a single gene DM : Also assume that the direction in which we travel is not important, so that DM = MD .
i) How many genes will be used in a chromosome of each individual if the number of cities is 10?
ii) How many genes will there be in the alphabet of the algorithm?
Maximize f ( x , y ) = 8 x + 6 y
Subject to 2x + 3y 6
-3x + 2y 3
2x + y 4
0 x
2
Using genetic algorithm