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Tutorial 6 1. List the relationships between biological and artificial networks. Biological Artificial Soma Node Dendrites Input Axon Output Synapse Weight Slow speed Fast speed 9 Many neurons (10 ) Few neurons (a dozen to hundreds of thousands) 2. How do weights function in an ANN? Weights define the impact that a given input has on a neuron in the next layer. As such, they embody what the network has learned so far. As a network learns, its weights are adjusted. 3.

What is the role of the summation function? The summation function determines the total input to a neuron by calculating the weighted sum of its individual input values. Its output is input to the transformation (transfer) function.

4.

What is the role of the transformation function? The transformation (or transfer) function determines the output of a neuron from its input (i.e., output of the summation function). Many possible transformation functions exist. Most are sigmoid functions. (A sigmoid function is a non-decreasing function whose value is in the range 0–1.) A simple transformation function produces an output of 0 for inputs up to a threshold value, an output of 1 from that value up.

5.

How does error between actual and predicted outcomes affect the value of weights in neural networks? It determines the changes that will be applied to the weights. To oversimplify somewhat, weights that contribute to correct answers are increased, while those that contribute to incorrect answers are decreased

6.

What are some of the design parameters for developing a neural network?

Number of layers (at least two; for n layers, there will be n–2 hidden layers) Number of nodes in each layer

Transformation functions, with parameters (e.g., threshold values) as applicable to each Initial weights Learning parameters: learning rate, momentum The last two bullets do not represent design parameters of the network itself. They are parameters of its development process. 7.

ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode. Learning in supervised mode is simpler. Since we know the desired output and the value of inputs as well as all the algorithms that express relationships in the system, it is possible to compute values of output for given weights. By comparing actual and desired weights one can see the difference. This difference can be reduced by adjusting the values of the weights. Ideally the differences should be driven to zero. Finding the ideal value of the weights is called the learning or training. In unsupervised situations, the desired outputs are not specified. Therefore, the process is only semi-automatic (i.e., a human must examine the result to determine when the training needs to stop). Weights and other parameters can be adjusted once the outputs are examined. 8. List the procedures of the learning algorithm. Answer: • Initialize weights with random values and set other parameters. • Read in the input vector and the desired output. • Compute the actual output via the calculations working forward through the layers. • Compute the error. • Change the weights by working backward from the output layer through the hidden layers. 9. There are about 50 to 150 billion neurons in the human brain and these neurons are partitioned into groups called ________. A) teams B) sects C) groups D) networks Answer: D 10. The ways neurons are organized are referred to as ________. A) topologies B) contour C) formation

D) configuration Answer: A 11. The summation function computes the ________ sums of all the input elements entering each processing element. A) weighted B) averaged C) total D) aggregated Answer: A 12. Which of the following is not a consideration in selecting a neural network structure? A) Selection of a topology B) Determination of input nodes C) Determination of output nodes D) Determination of weighting functions Answer: D

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What is the role of the summation function? The summation function determines the total input to a neuron by calculating the weighted sum of its individual input values. Its output is input to the transformation (transfer) function.

4.

What is the role of the transformation function? The transformation (or transfer) function determines the output of a neuron from its input (i.e., output of the summation function). Many possible transformation functions exist. Most are sigmoid functions. (A sigmoid function is a non-decreasing function whose value is in the range 0–1.) A simple transformation function produces an output of 0 for inputs up to a threshold value, an output of 1 from that value up.

5.

How does error between actual and predicted outcomes affect the value of weights in neural networks? It determines the changes that will be applied to the weights. To oversimplify somewhat, weights that contribute to correct answers are increased, while those that contribute to incorrect answers are decreased

6.

What are some of the design parameters for developing a neural network?

Number of layers (at least two; for n layers, there will be n–2 hidden layers) Number of nodes in each layer

Transformation functions, with parameters (e.g., threshold values) as applicable to each Initial weights Learning parameters: learning rate, momentum The last two bullets do not represent design parameters of the network itself. They are parameters of its development process. 7.

ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode. Learning in supervised mode is simpler. Since we know the desired output and the value of inputs as well as all the algorithms that express relationships in the system, it is possible to compute values of output for given weights. By comparing actual and desired weights one can see the difference. This difference can be reduced by adjusting the values of the weights. Ideally the differences should be driven to zero. Finding the ideal value of the weights is called the learning or training. In unsupervised situations, the desired outputs are not specified. Therefore, the process is only semi-automatic (i.e., a human must examine the result to determine when the training needs to stop). Weights and other parameters can be adjusted once the outputs are examined. 8. List the procedures of the learning algorithm. Answer: • Initialize weights with random values and set other parameters. • Read in the input vector and the desired output. • Compute the actual output via the calculations working forward through the layers. • Compute the error. • Change the weights by working backward from the output layer through the hidden layers. 9. There are about 50 to 150 billion neurons in the human brain and these neurons are partitioned into groups called ________. A) teams B) sects C) groups D) networks Answer: D 10. The ways neurons are organized are referred to as ________. A) topologies B) contour C) formation

D) configuration Answer: A 11. The summation function computes the ________ sums of all the input elements entering each processing element. A) weighted B) averaged C) total D) aggregated Answer: A 12. Which of the following is not a consideration in selecting a neural network structure? A) Selection of a topology B) Determination of input nodes C) Determination of output nodes D) Determination of weighting functions Answer: D

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