Generalization in multi-layer feed forward nueral networks employing the back propagation algorithm/
Neural networks have been around for years, but only recently has there been great interest in them. Neural network models are composed of many computational elements operating in parallel. These elements are called nodes and are nonlinear, typically analog. A type of network which has been used successfully as a classifier is the multi-layer perceptron. Multi-layer perceptrons are feed forward networks with one or more layers of nodes between the input and output nodes. Each node passes the result through a nonlinearity (activation function) to the next layer. This paper uses a neural network as described above to study the capability of the network to generalize. Using English characters as inputs, the network is analyzed for its ability to identify characters that have translation offsets and characters that have been rotated. Also studied is the networks immunity to three cases of noise. In the first case the network is trained with pure characters and noisy characters; then the network's response to characters with an intermediate amount of noise is studied. In the second case the network is trained to pure characters; then the network's response to increasing of noise amounts is observed. Finally an effort is made to train the network with noisy characters, then study the response of the network to noisy characters.
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Master of Science (M.S.)
College of Engineering
Electrical Engineering and Communication Sciences
Length of Campus-only Access
Masters Thesis (Open Access)
Orlando (Main) Campus
Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic
Gerrity, Francis Joseph, "Generalization in multi-layer feed forward nueral networks employing the back propagation algorithm/" (1989). Retrospective Theses and Dissertations. 4141.