Artificial Neural Networks: AND Function

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18 Questions

What is the output of the perceptron when x1 = 1, x2 = -1, w1 = 2, w2 = 2, b = -2, and θ = 0?

-1

What type of units are used in the example of the AND function?

Bipolar units

What is the condition for the output of the perceptron to be 1?

The weighted sum is greater than the threshold

What is the problem with Hebbian learning?

It cannot learn nonlinearly separable functions

What is the purpose of the threshold θ in the perceptron?

To determine the output of the perceptron

What is the advantage of using bipolar units?

They can learn certain linearly separable functions more easily

What is the role of the bias unit in the Hebb net for the AND function?

It is used to introduce a constant input to the neuron

What is the purpose of the bias term b in the single layer neural network?

To represent the weight from a special unit with constant output 1

What is the update rule for the weights in Hebbian learning?

wij(new) = wij(old) + xi * y

What type of activation function is used for binary data in Hebbian learning?

Binary step function

What is the decision boundary in the case of n = 2, b != 0, and θ = 0?

A line

What is the key characteristic of error-driven learning?

Weights are updated based on the error between the predicted and actual output

What is the condition for the output of the neural network to be 1?

y_in ≥ θ

What is the condition for linear separability in a perceptron?

The data points can be separated by a single hyperplane

What is the Hebbian learning law in the context of Artificial Neural Networks?

wij increases only when both I and j are 'on'

What is the update rule for the weights in the Hebb net learning algorithm?

wij(new) = wij(old) + xi*y

How many times is each training sample used in the Hebb net for the AND function?

Once

What is the primary concept behind Hebb's claim in his book 'The Organization of Behavior'?

Behavior changes are primarily due to the changes of synaptic strengths between neurons

This quiz assesses the understanding of artificial neural networks, specifically the AND function, using examples with bipolar units and weight changes.

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