Neural network analysis is proposed and evaluated as a method of analysis of marine biological data, specifically images of plankton specimens. The quantification of the various plankton species is of great scientific importance, from modelling global climatic change to predicting the economic effects of toxic red tides. A preliminary evaluation of the neural network technique is made by the development of a back-propagation system that successfully learns to distinguish between two co-occurring morphologically similar species from the North Atlantic Ocean, namely Ceratium arcticum and C. longipes. Various techniques are developed to handle the indeterminately labelled source data, pre-process the images and successfully train the networks. An analysis of the network solutions is made, and some consideration given to how the system might be extended.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21280号 / 情博第674号 / 新制||情||116(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 田中 利幸, 教授 山下 信雄, 准教授 増山 博之, 教授 笠原 正治 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This thesis investigates the functionality of the units used in connectionist Artificial Intelligence systems. Artificial Neural Networks form the foundation of the research and their units, Artificial Neurons, are first compared with alternative models. This initial work is mainly in the spatial-domain and introduces a new neural model, termed a Taylor Series neuron. This is designed to be flexible enough to assume most mathematical functions. The unit is based on Power Series theory and a specifically implemented Taylor Series neuron is demonstrated. These neurons are of particular usefulness in evolutionary networks as they allow the complexity to increase without adding units. Training is achieved via various traditiona and derived methods based on the Delta Rule, Backpropagation, Genetic Algorithms and associated evolutionary techniques. This new neural unit has been presented as a controllable and more highly functional alternative to previous models. The work on the Taylor Series neuron moved into time-domain behaviour and through the investigation of neural oscillators led to an examination of single-celled intelligence from which the later work developed. Connectionist approaches to Artificial Intelligence are almost always based on Artificial Neural Networks. However, another route towards Parallel Distributed Processing was introduced. This was inspired by the intelligence displayed by single-celled creatures called Protoctists (Protists). A new system based on networks of interacting proteins was introduced. These networks were tested in pattern-recognition and control tasks in the time-domain and proved more flexible than most neuron models. They were trained using a Genetic Algorithm and a derived Backpropagation Algorithm. Termed "Artificial BioChemical Networks" (ABN) they have been presented as an alternative approach to connectionist systems.