EPO620 TTEST TRIAL

From the shown diagram, which analogy is correct?

• Soma --> Input

• Axon --> Input

• Dendrite --> Input

• Synapse --> Input

Which statement is correct?

• Output

  layer: the layer that makes a prediction

• Input

  layer: take input from another layer and pass output

  to another layer

• Hidden

  layers: directly connect to input data or produce outputs

• Input

  layer: has highest neuron than other layer

Which transfer function is always used in ANNs (Artificial Neural Networks)?
i. hard-limiter

ii. trigonometry

iii. polynomial

iv. sigmoid

• All above

• i, ii and iv

• i, ii and iii

• i and iv

What type of transfer function is shown?

• Linear

• Hard-limiter

• Sigmoid

• Hyperbolic Tangent

Which statement is correct?

• ALL

  HIDDEN LAYERS typically use the different transfer function

• The

  transfer functions that can be used for HIDDEN LAYERS is polynomial

• Selection

  of transfer function in the HIDDEN LAYER will control how

  well the neural network learns the training data

• The HIDDEN LAYERS should use only linear activation function

Net output n, is found using a following formula:
n = Wp + b

Given inputs, p and weights, w as follows:
p = [4 10 6]

W = [0.5 0 0.4]

and bias = 1

Determine the net output n, if activation function is SIGMOID

• 0.980

• 0.885

• 0.995

• 0.955

What is the primary purpose of input data in the TRAINING of Artificial Neural Networks (ANN)?

• Input data is used to evaluate the performance of the ANN.

• Input data helps in making predictions with the ANN.

• Input data is used for ANN learning process through backpropagation algorithm.

• Input data is only used to set the initial values of weights and biases.

What is the purpose of input data during the TESTING phase of an Artificial Neural Network (ANN)?

• To fine-tune the ANN's weight and bias values.

• To produce the predicted output during ANN simulation.

• To verify the initial settings of the ANN.

• To ensure that the ANN training data is reused for testing.

Why is normalization applied to input variables in data preprocessing?

• To increase the complexity of the input data.

• To ensure that input variables have different units.

• To make data analysis more challenging.

• To ensure that all input variables are at a comparable range, treating every bit of data in a consistent manner.

How should the learning rate (α) be adjusted during training in an Artificial Neural Network (ANN)?

• Increase the learning rate when the sum squared error alternates.

• Increase the learning rate when the sum squared error is the same for several consequent epochs.

• Decrease the learning rate when the sum squared error is the same for several consequent epochs.

• Decrease the learning rate when the sum squared error alternates.