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A New Design Approach For Numeric-To-Symbolic Conversion Using Neural Networks, Zibin Tang
A New Design Approach For Numeric-To-Symbolic Conversion Using Neural Networks, Zibin Tang
Dissertations and Theses
A new approach is proposed which uses a combination of a Backprop paradigm neural network along with some perceptron processing elements performing logic operations to construct a numeric-to-symbolic converter. The design approach proposed herein is capable of implementing a decision region defined by a multi-dimensional, non-linear boundary surface. By defining a "two-valued" subspace of the boundary surface, a Backprop paradigm neural network is used to model the boundary surf ace. An input vector is tested by the neural network boundary model (along with perceptron logic gates) to determine whether the incoming vector point is within the decision region or not. …
Neural Network Character Recognition With A 2-D Fourier Transform Preprocessor, Daqiao Du
Neural Network Character Recognition With A 2-D Fourier Transform Preprocessor, Daqiao Du
Dissertations and Theses
In pattern recognition applications, it is usually important that the same identification be given for a pattern, independent of a variety of positions, rotations and /or distortions of the pattern within the recognition device's field of view. This research relates to development of a preprocessor for a neural network character recognition system, where the role of the preprocessor is to assist in minimizing the difficulties related to variations of position and rotations of a character within the field of view. The preprocessor explored here was suggested in 1970' (Lendaris & Stanly, 1970), and is implemented here with more recent advances …