Title page for ETD etd-07142006-093118


Type of Document Master's Thesis
Author Rishiyur, Adityan V
URN etd-07142006-093118
Title Instantaneously Trained Neural Networks with Complex and Quaternion Inputs
Degree Master of Science (M.S.)
Department Electrical & Computer Engineering
Advisory Committee
Advisor Name Title
Subhash Kak Committee Chair
Ashok Srivastava Committee Member
Hsiao-Chun Wu Committee Member
Keywords
  • artificial intelligence
  • image understanding
  • Kak neural network
Date of Defense 2006-05-15
Availability unrestricted
Abstract
Neural network architectures such as backpropagation networks, perceptrons or generalized Hopfield networks can handle complex inputs but they require a large amount of time and resources for the training process. This thesis investigates instantaneously trained feedforward neural networks that can handle complex and quaternion inputs. The performance of the basic algorithm has been analyzed and shown how it provides a plausible model of human perception and understanding of images. The motivation for studying quaternion inputs is their use in representing spatial rotations that find applications in computer graphics, robotics, global navigation, computer vision and the spatial orientation of instruments. The problem of efficient mapping of data in quaternion neural networks is examined. Some problems that need to be addressed before quaternion neural networks find applications are identified.
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