Adaptive pattern recognition in a real-world environment
Abstract
This thesis introduces and explores the notion of a real-world environment
with respect to adaptive pattern recognition and neural network systems. It
then examines the individual properties of a real-world environment and
proposes Continuous Adaptation, Persistence of information and Context-sensitive
recognition to be the major design criteria a neural network system in
a real-world environment should satisfy.
Based on these criteria, it then assesses the performance of Hopfield networks
and Associative Memory systems and identifies their operational limitations.
This leads to the introduction of Randomized Internal Representations, a novel
class of neural network systems which stores information in a fully
distributed way yet is capable of encoding and utilizing context.
It then assesses the performance of Competitive Learning and Adaptive
Resonance Theory systems and again having identified their operational
weakness, it describes the Dynamic Adaptation Scheme which satisfies all
three design criteria for a real-world environment.
Type
Thesis, PhD Doctor of Philosophy
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