AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

QUESTIONS FOR REVIEW
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neuron. Although competitive learning was proposed in the early 1970s, it
was largely ignored until the late 1980s, when Teuvo Kohonen introduced a
special class of artificial neural networks called self-organising feature maps.
He also formulated the principle of topographic map formation which states
that the spatial location of an output neuron in the topographic map
corresponds to a particular feature of the input pattern.
. The Kohonen network consists of a single layer of computation neurons, but
it has two different types of connections. There are forward connections from
the neurons in the input layer to the neurons in the output layer, and lateral
connections between neurons in the output layer. The lateral connections are
used to create a competition between neurons. In the Kohonen network, a
neuron learns by shifting its weights from inactive connections to active ones.
Only the winning neuron and its neighbourhood are allowed to learn. If a
neuron does not respond to a given input pattern, then learning does not
occur in that neuron.
Questions for review
1 How does an artificial neural network model the brain? Describe two major classes of
learning paradigms: supervised learning and unsupervised (self-organised) learning.
What are the features that distinguish these two paradigms from each other?
2 What are the problems with using a perceptron as a biological model? How does the
perceptron learn? Demonstrate perceptron learning of the binary logic function OR.
Why can the perceptron learn only linearly separable functions?
3 What is a fully connected multilayer perceptron? Construct a multilayer perceptron with
an input layer of six neurons, a hidden layer of four neurons and an output layer of two
neurons. What is a hidden layer for, and what does it hide?
4 How does a multilayer neural network learn? Derive the back-propagation training
algorithm. Demonstrate multilayer network learning of the binary logic function
Exclusive-OR.
5 What are the main problems with the back-propagation learning algorithm? How can
learning be accelerated in multilayer neural networks? Define the generalised delta
rule.
6 What is a recurrent neural network? How does it learn? Construct a single six-neuron
Hopfield network and explain its operation. What is a fundamental memory?
7 Derive the Hopfield network training algorithm. Demonstrate how to store three
fundamental memories in the six-neuron Hopfield network.
8 The delta rule and Hebb’s rule represent two different methods of learning in neural
networks. Explain the differences between these two rules.
9 What is the difference between autoassociative and heteroassociative types of
memory? What is the bidirectional associative memory (BAM)? How does the BAM
work?