Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/13130
Title: Improving neural networks for pattern recognition and function approximation
Authors: Zhang, Ximin
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 1999
Abstract: This thesis studies various issues related to artificial neural networks for pattern recognition and function approximation with the aim to enhance its capability and to improve its performance. It proposes a novel method for globally finding good minima and optimizing the Correct Classification Rate(CCR), and a novel algorithm for network construction and weight initialization. The thesis also an-alyzes the fundamentals of Time-Delay Neural Network(TDNN) and presents an augmented TDNN (ATDNN) for frequency and scale invariant sequence classification.
URI: http://hdl.handle.net/10356/13130
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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