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Scholars Day 2008, Wednesday, April 9

Artificial Neural Networks and their Applications

Artificial Neural Networks (ANN) are used in computing reasonable solutions to NP-hard problems. An artificial neuron has a number of input stimuli, and an output. Each neuron calculates an activation-value depending on the inputs and its internal weights and an output depending on the activation and a threshold. An ANN is a network of neurons where outputs of some neurons become inputs to others. ANN's are trained by presenting them with examples of a concept or by unsupervised learning. An ANN learns by adjusting its weights according a learning rule. They have been used to solve a variety of problems including, character recognition, detection of patterns, non-linear optimization, etc. This study explores ANN's and their applications to various problems. We have implemented several ANN topologies including: feed-forward, back-propagation, Kohonen, Hopfield, etc. Our implementation was successful in recognizing several English characters. We used the Hopfield-Tank model to implement a solution to the traveling-seller problem. We also compared this approach to other evolutionary techniques.

Presenter: Steven Klein (Undergraduate Student)
Topic: Computer Science
Location: 102 Edwards
Time: 9 am (Session I)

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