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<p>This thesis investigates the properties of a software package called HNeT (Holographic/Quantum Neural Technology) which is based on the use of an artificial intelligence tool called Neural Networks. The basis for the investigation of this software is to establish its reliability, effectiveness and efficiency. Neural technology is a technological replication of the biological neural system designed to learn data patterns and process the data (stimulus) and then generate a response based on the memory of the data. HNeT theory is fundamentally different from the standard Artificial Neural System (ANS) in that it uses complex scalars to evaluate internal mappings of one set of values (stimuli) to another set of values (responses). HNeT employs a process known as enfolding, which allows the learning and subsequent recall of many stimulus-response associations to be compressed into a single HNeT neuron cell improving the speed of learning and recall accuracy as well as reducing storage requirements. Whereas the traditional ANS stores stimulus patterns separately as a reference template within a cell and are compared one at a time to a new incoming stimulus response pattern which in this case, requires larger amounts of memory.
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<br>Subjects:</div>
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Langford, Darryl</div>
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