Bibliography: p. 199-224. / xviii, 224 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis reviews models of the simple cell receptive field profile (RFP) and its variation over the simple cell population, and uses artifical neural networks to investigate the multi-dimensional signal processing role of the RFP in the formation of a cortical representation of the visual image. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1994?

"April 2004" / Bibliography: p. 285-299. / xxviii, 299p : ill., map ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Artificial neural networks (ANNs), trained to make short term forecasts of algal blooms in lakes and rivers, are potentially useful decision making tools for the operational management of eutrophication. This thesis addresses the question of whether a standardised, gemeric ANN model representation can be developed to achieve this goal. It is argued that four requirements need to be addressed: i) compatibility of models with existing water quality monitoring regimes, ii) stability and repeatability of training outcomes, iii) realistic and meaningful estimates of model performance, and iv) explanation of predictions. / Thesis (Ph.D.)--University of Adelaide, School of Earth and Environmental Sciences, Discipline of Environmental Biology, 2004

Modelling of stream macroinvertebrate communities has been widely accepted as an interesting and powerful tool to support water quality assessment and management. Stream Decision Support Framework (SDSF) offers an alternative approach to the current statistical models as Australian River Assessment Scheme (AusRivAs) for the derivation of scientific basis to support management applications regarding fresh water systems. Implementation of Artificial Neural Networks (ANNs) offers a possibility to overcome constraints of the statistical methods in dealing with high nonlinearity of stream data. This thesis includes several case studies illustrating application of Self Organising Map (SOM) and Multilayer Perceptron (MLP) neural networks to various tasks involving analysis, assessment and prediction of stream macroinvertebrates in three Australian states. The data for this study have been provided by the Queensland Department of Natural Resources (NR&M), EPA Victoria and the Department of Land and Water Conservation, New South Wales (NSW). SDSF approach utilises predictive models for both 'referential' and 'dirty-water' approaches. Applicability and high accuracy of ANN models for the purpose of prediction both occurrence of individual taxa and taxonomic richness of stream macroinvertebrates have been demonstrated using data from Victoria and NSW. A comprehensive analysis of salinity sensitivity of stream macroinvertebrate has been demonstrated using both types of ANNs plus statistical methods, and pressure specific Salinity Index was suggested as a measurement of changes within macroinvertebrate communities in response to the secondary salinisation. Scenario analysis of the combined effect of increasing salinity and nutrient load demonstrated predictability and ecological meaningfulness of the Salinity Index. Application of SOM has been demonstrated using the data from Queensland and Victoria in order to analyse natural variability of macroinvertebrate communities between reference sites. SOM component planes provided a valuable insight into the relationships between abiotic variables (as water quality and geoclimatic factors) and distribution of taxa and trophic structure of macroinvertebrate communities. Potential of SOM as data exploration tool has been also demonstrated for the analysis of the output of scenario simulation in order to understand the difference in response to salinisation in different sites. Flexibility and potential of SDSF have been illustrated by using the combination of SOM and MLP, and combination of ANNs with statistical methods. Application of both SOM and Canonical Correspondence Analysis allowed the extraction of additional information and provided convenient visualisation of the relationships between water quality factors and the structure of macroinvertebrate communities. In general, SDSF provides convenient, flexible and accurate approach for the analysis, assessment and prediction of stream biota. In addition to the freedom from the limitations inherent to the traditional statistical methods it allows many more options than currently used modelling frameworks, namely: highly accurate predictions using both 'referential' and 'dirty-water' approaches, sensitivity analysis, scenario analysis and pattern exploration using SOM. / Thesis (Ph.D.)--School of Earth & Environmental Sciences, 2005.

Thesis (Ph. D.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 6, 2007) Includes bibliographical references.

Thesis (M. Phil.)--University of Hong Kong, 1999. / Also available in print.

Thesis (Ph.D.)--Utrecht University, 2006. / Includes bibliographical references (p. [133]-137).

Most statistical surveys and data collection studies encounter missing data. A common solution to this problem is to discard observations with missing data while reporting the percentage of missing observations in different output tables. Imputation is a tool used to fill in the missing values. This dissertation introduces the missing data problem as well as traditional imputation methods (e.g. hot deck, mean imputation, regression, Markov Chain Monte Carlo, Expectation-Maximization, etc.). The use of artificial neural networks (ANN), a data mining technique, is proposed as an effective imputation procedure. During ANN imputation, computational effort is minimized while accounting for sample design and imputation uncertainty. The mechanism and use of ANN in imputation for complex survey designs is investigated. Imputation methods are not all equally good, and none are universally good. However, simulation results and applications in this dissertation show that regression, Markov chain Monte Carlo, and ANN yield comparable results. Artificial neural networks could be considered as implicit models that take into account the sample design without making strong parametric assumptions. Artificial neural networks make few assumptions about the data, are asymptotically good and robust to multicollinearity and outliers. Overall, ANN could be time and resources efficient for an experienced user compared to other conventional imputation techniques. / Graduation date: 2005

Bayesian networks are used for building intelligent agents that act under uncertainty. They are a compact representation of agents' probabilistic knowledge. A Bayesian network can be viewed as representing a factorization of a full joint probability distribution into the multiplication of a set of conditional probability distributions. Independence of causal influence enables one to further factorize the conditional probability distributions into a combination of even smaller factors. The efficiency of inference in Bayesian networks depends on how these factors are combined. Finding an optimal combination is NP-hard. We propose a new method for efficient inference in large Bayesian networks, which is a combination of new representations and new combination algorithms. We present new, purely multiplicative representations of independence of causal influence models. They are easy to use because any standard inference algorithm can work with them. Also, they allow for exploiting independence of causal influence fully because they do not impose any constraints on combination ordering. We develop combination algorithms that work with heuristics. Heuristics are generated automatically by using machine learning techniques. Empirical studies, based on the CPCS network for medical diagnosis, show that this method is more efficient and allows for inference in larger networks than existing methods. / Graduation date: 1999

The main focus of this work is on the problem of existence of nonlinear optimal controllers realizable by artificial neural networks. Theoretical justification, currently available for control applications of neural networks, is rather limited. For example, it is unclear which neural architectures are capable of performing which control tasks. This work addresses applicability of neural networks to the synthesis of approximately optimal state feedback. Discrete-time setting is considered, which brings extra regularity into the problem and simplifies mathematical analysis. Two classes of optimal control problems are studied: time-optimal control and optimal control with summable quality index. After appropriate relaxation of the optimization problem, the existence of a suboptimal feedback mapping is demonstrated in both cases. It is shown that such a feedback may be realized by a multilayered network with discontinuous neuron activation functions. For continuous networks, similar results are obtained, with the existence of suboptimal feedback demonstrated, except for a set of initial states of an arbitrarily small measure. The theory developed here provides basis for an attractive approach of the synthesis of near-optimal feedback using neural networks trained on optimal trajectories generated in open loop. Potential advantages of control based on neural networks are illustrated on application to stabilization of interconnected power systems. A nearly time-optimal controller is designed for a single-machine system using neural networks. The obtained controller is then utilized as an element of a hierarchical control architecture used for stabilization of a multimachine power transmission system. This example demonstrates applicability of neural control to complicated, nonlinear dynamic systems. / Graduation date: 1995

Thesis (Ph. D.)--Washington State University, May 2007. / Includes bibliographical references.