Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429032
Title: An infrastructure for neural network construction
Author: Stewart, Richard
ISNI:       0000 0001 3485 3624
Awarding Body: Brunel University
Current Institution: Brunel University
Date of Award: 2005
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Abstract:
After many years of research the area of Artificial Intelligence is still searching for ways to construct a truly intelligent system. One criticism is that current models are not 'rich' or complex enough to operate in many and varied real world situations. One way to tackle this criticism is to look at intelligent systems that already exist in nature and examine these to determine what complexities exist in these systems and not in the current Al models. The research begins by presenting an overview of the current knowledge of Biological Neural Networks, as examples of intelligent systems existing in nature, and how they function. Artificial Neural networks are then discussed and the thesis examines their similarities and dissimilarities with their biological counterparts. The research suggests ways that Artificial Neural Networks may be improved by borrowing ideas from Biological Neural Networks. By introducing new concepts drawn from the biological realm, the construction of the Artificial Neural Networks becomes more difficult. To solve this difficulty, the thesis introduces the area of Evolutionary Algorithms as a way of constructing Artificial Neural Networks. An intellectual infrastructure is developed that incorporates concepts from Biological Neural Networks into current models of Artificial Neural Networks and two models are developed to explore the concept that increased complexity can indeed add value to the current models of Artificial Neural Networks. The outcome of the thesis shows that increased complexity can have benefits in terms of learning speed of an Artificial Neural Network and in terms of robustness to damage.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.429032  DOI: Not available
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