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This paper describes the authors' recent research on the theories about internal workings of feed-forward artificial neural networks (ANNs). Knowledge of the inner workings of ANNs progresses with the evolution of ANN modelling techniques. In the early stages, neural networks consisted of perceptrons and were easy to interpret. Since the emergence of backpropagation learning in the 1980s, ANN models have become much more complex. Even today, many users still consider ANN models as black-box type models. In this paper, a systematic approach is demonstrated to study the behaviours of ANN models in an attempt to open up the ANN "black box". The authors emphasise that feed-forward ANN models are functions, and use a graphical interpretation technique to open the ANN models and show the detailed activities of each inner component through several case studies. These studies include how the training process changes the internal components of an ANN model, how noise impacts the training process, and how the sensitivity of ANN models is affected by the training data. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) technology has evolved from the experimental stage into actual industrial applications. To achieve this significant transition, careful planning and adjustment are required. This paper illustrates such an example in the water treatment industry. The project objective is to upgrade the ANN models from a previous research project and install the system on-line in the Rossdale Water Treatment Plant in Edmonton, Alberta, Canada, to forecast raw water colour one day ahead. The article discusses the important issues and techniques to upgrade the neural network model to the actual application. Furthermore, sufficient communication is also required between the designers and the users to address the applicability and user friendly issues in model implementation. Failure in communication can render the whole process ineffective. Possible improvements are also recommended for the future on-line applications. [ABSTRACT FROM AUTHOR]

Although the artificial neural network (ANN) approach has been used in various disciplines of engineering since the 1980s, its use in the oil sands extraction industry is quite new and has great potential. This paper demonstrates two important ANN modelling techniques that will be very useful to the oil sand industry through a case study. First, the ANN pattern recognition approach is illustrated to categorize a large oil sand processing database consisting of experimental data from the last 20 years. Second, within each category, the authors demonstrate how to follow a general protocol to build ANN models that are capable of predicting the primary recovery and the primary froth quality for the oil sand treatment process with fairly good accuracy. To verify the reliability of the ANN models, besides the regular statistical and graphical analysis, the authors also conducted a sensitivity analysis to test the response logic, parameter interaction, and extrapolation capability of the ANN models. As shown in the paper, these tests are both satisfactory and interesting. With sufficient accuracy and robustness in performance, these ANN models can be used to evaluate both the qualitative and the quantitative response of the oil sand treatment process to the known key parameters, which, in turn, can then be used to optimize the oil sand treatment process. Furthermore, these ANN models can be linked with the geological survey results to estimate the production potential of an oil sand ore field and to manage the cost of stockpiling the chemicals for the treatment process. [ABSTRACT FROM AUTHOR]

Within the context of California's public report ofcoronary artery bypass graft(CABG) surgery outcomes, we first thoroughly review popular statistical methods for profiling healthcare providers. Extensive simulation studies are then conducted to compare profiling schemes based on hierarchical logistic regression (LR) modeling under various conditions. Both Bayesian and frequentist's methods are evaluated in classifying hospitals into ‘better’, ‘normal’ or ‘worse’ service providers. The simulation results suggest that no single method would dominate others on all accounts. Traditional schemes based on LR tend to identify too many false outliers, while those based on hierarchical modeling are relatively conservative. The issue of over shrinkage in hierarchical modeling is also investigated using the 2005–2006 California CABG data set. The article provides theoretical and empirical evidence in choosing the right methodology for provider profiling. [ABSTRACT FROM AUTHOR]