The effects of using problem knowledge in a neural network for image processing tasks : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand
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Date
2001
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Massey University
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Abstract
This thesis is concerned with aspects of computational intelligence. Computational intelligence is a new paradigm of artificial intelligence based on biological intelligence. Computational Intelligence explores the potential for biologically inspired intelligent adaptive machines and behavior. A relatively new and important sub discipline within the field of computational intelligence, is that of neural networks. Neural networks are networks of artificial neurons with a high degree of interconnectivity. The networks capture and accumulate knowledge as the pattern of weights in the interconnections between neurons. Neural networks are iteratively trained to perform tasks by "learning" from examples. These networks are often slow to train. One of the reasons for this is that the starting weights of the network are conventionally not related to the problem being solved. In this thesis a methodology is explored that maps problem knowledge to the starting weights of a fuzzy neural network window filter (FuNNWF). The FuNNWF architecture was developed from the combination of fuzzy logic and artificial neural networks for image processing tasks. The effects of the use of problem knowledge on FuNNWF training are investigated. The problem knowledge mapping procedure is extended from boolean rule mapping to conditional rule mapping, which allows better representation of the problems. Four real world image processing problems are investigated using the new weight initialization methodology. The experiments reported in this thesis demonstrate that the use of problem knowledge improves the robustness and convergence of the neural network. It is also shown that the use of the methodology is most effective on network training when the training data is noisy, unreliable and ambiguous.
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Digital and hard copy imperfect: P.1 missing
Keywords
Soft computing, Computational intelligence, Neural networks (Computer Science), Image processing, Computer science