Download Honors Thesis Proposal

Honors Thesis Proposal
Iman Sen
Class of 2003
Advisor: Professor Searleman
22nd January, 2002
Connectionism: Applications for Artificial Neural Networks
My proposed thesis is to thoroughly investigate the field of artificial neural networks,
which is a sub field of Artificial Intelligence. So what is Artificial Intelligence, that much-hyped
yet unclear realm which promises us intelligent robot mates in the near future? It is simply a field
comprised of different approaches that are used to make computers more intelligent. Artificial
Intelligence techniques range widely from well-understood searches and logical relationship
representations, to more obscure and less understood methods, such as large decision trees. One
approach for problem solving simulates the functioning of the very center of human intelligence,
the human brain. This is the field of artificial neural networks.
Although the origin of artificial neural networks or ANNs was based on mimicking the
human brain, the basic idea behind ANNs is to find a new means of solving problems, based on
parallel processing.
An ANN is a network of many very simple processing units. This structure is similar to
its biological counterpart, where a single neuron is the basic unit, and is part of a complex neural
network (Fig 1). ANNs generally have a layered structure, with each layer having a certain
number of simple units (Fig 2). These units are connected to each other, and the connections have
certain weights associated with them. ANNs have some sort of training rule whereby these
weights are adjusted on the basis of presented inputs or patterns. In other words, neural networks
can "learn" from examples, by adjusting their connection weights. In principle, ANNs can
compute any computable function, that is they can do everything a normal digital computer can
do [2]. In practice, ANNs are especially useful for mapping problems which allow some error,
and have lots of example data available (to train the network thoroughly). Some of the popular
applications of ANNs are in voice recognition, image recognition, character recognition, car
navigation, data compression, chess and backgammon.
My Proposed Study
ANNs can vary based on the connections between the processing units (whether each unit
is connected to all other units, or only to selected units), the direction of communication
{unidirectional or bidirectional), and the functions used for adjusting the weights, and estimating
the activation of the units. My proposed study is to investigate which type of ANN is best suited
for, or has been most successful in, which application. To put it another way, I plan to point out
the specific properties of certain ANNs which make them particularly suitable for certain
applications. My findings will also be demonstrated through programs that simulate ANNs.
Therefore, my study will be two-folded, firstly to research into ANNs thoroughly, and then to use
ANN simulators to exhibit the success of different ANNs on different applications.
Here is a likely timeline:
Starting Bibliography
BOOKS
[1] Luger, GF and Stubblefield, WA 1998. Artificial Intelligence: Structures and Strategies for
Complex Problem Solving. Reading, MA: Addison-Wesley.
[2] Russell, SJ and Norvig, P 1995.Artificial Intelligence: A Modern Approach. Upper Saddle
River, NJ: Prentice Hall.
[3] Dean T, Allen, J and Aloimonos, Y 1995. Artificial Intelligence: Theory and Practice.
Redwood City, CA: Benjamin Cummings.
[4] Welstead, ST 1994. Neural Network and Fuzzy Logic Applications in C/C++. New York,
NY: John Wiley and Sons.
[5] Bechtel, W and Abrahamsen, A 1991. Connectionism and the Mind: An introduction to
parallel processing in networks. Cambridge, MA: Basil Blackwell.
SOME WEB RESOURCES
Carnegie Mellon Neural Net Repository
http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/0.html
Comprehensive link to numerous ANN journals on the web
http://www.emsl.pnl.gov/2080/proj/neuron/neural/journals.html