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Model Predictive Control (MPC) is an application of control that is highly popular due to its sensible approach and its ease of implementation. These qualities give MPC an advantage over Linear Quadratic (LQ) control, even though LQ will result in the optimal result where feasible. Recent advancements have resulted in greater computational power, which has given rise to the development of more complicated MPC algorithms, but there are instances when the complexity of the calculations involved will result in the amount of computations involved or ill-conditioning of the problem.

Expert systems technology is a discipline of artificial intelligence that has recently emerged from the research environment and is currently making significant inroads into business and industry. The proponents of expert systems make many attractive claims. Two of the claims are that expert systems can capture the knowledge of the experts and can be programmed by non-programmers. To date, most uses of this technology in the process industry are in off-line applications, that is, applications that are not directly tied to operating environments. Moreover, those that are used in on-line environments are used as advisors and only suggest changes, human operators are required to close-the-loop to the process. This technology should be applicable to operating environments and should the claims of its proponents be valid, then it should also be better than existing tools currently in use. In an operating environment it could use the knowledge of experienced operators as an intelligent controller and apply it directly to a process without the intervention of human operators. In this thesis the prospect of using an expert system as an intelligent controller is investigated. The thesis offers background to expert systems, how expert systems are related to artificial intelligence and what the generic components of an expert system are. As a test of the technology an expert system was developed as an intelligent controller in a mineral processing application. The prototype expert system was developed as a supervisory controller in the copper flotation circuit in the concentrator at Brenda Mines Ltd.. The expert system operated on-line and controlled the process in real-time. It read sensor data and using the operating experience of Brenda's flotation operators, manipulated regulatory controller setpoints as deemed necessary. The expert system was able to manipulate directly reagent flowrates and process air flowrates. Also, it suggested changes to other process variables not directly under its control. The manipulated variables were collector and frother reagent flowrates, the air flowrate to a bank of scavenger flotation cells and the air flowrate to four flotation columns. The operators were allowed to, and sometimes required to, intervene in instances of large process upsets. Results of a month long trial period in the flotation circuit indicated the success of the application. The expert system was indeed able to maintain metallurgical performance at a level approximating that of the operating experts, however, no direct comparison between the performance of the circuit under expert system control, versus performance under operator control, was possible. A rudimentary comparison was made between the circuit's performance under expert system control and its historic performance. The results were favourable. A less tangible measure, though as important, were the attitudes of the operators toward the system. Almost unanimously, the operators felt the system eased the task of circuit operation and was responsible for better circuit performance. / Applied Science, Faculty of / Mining Engineering, Keevil Institute of / Graduate

The degree work is focused on analysis of inflation with help of suitable econometric models. Inflation with it's forms and possibilities of measuring is described at the beginning of the paper. There is mentioned an importance of monitoring and analysing inflation in view of Czech national bank. Consequently there are described characteristics of time series, which are important from viewpoint of construction of econometric models. Next part of this paper is focused on characterization of econometrics models. At first there is vector autoregression model, in this connection there is discussed the essence of Granger causality and impulse reaction. There are also noticed both error correction model and vector error correction model. The empirical part of degree work involves the use of these models on selected macroeconomic time series of the Czech republic. The objective is to analyze the relationship between inflation and other individual macroeconomic quantities. There is established the optimal vector autoregressive model and the results of Granger causality and impulse reaction are interpretated. Both error correction model and vector error correction model examining cointegration are also applied.

We develop and test asset pricing model formulations that are simultaneously conditional and nonlinear. Formulations based upon five popular asset pricing models are tested against the widely studied Fama and French (1993) twenty-five size and book-to-market sorted portfolios. Test results indicate that the conditional nonlinear specification of the Fama and French (1993) three state variable model (FF3) is the only specification not rejected by the data and thus capable of pricing the "size" and "book-to-market" effects simultaneously. The pricing performance of the FF3 conditional nonlinear pricing kernel is corifirmed by robustness tests on out-of-sample data as well as tests with alternative instrumental and conditioning variables. While Bansal and Viswanathan (1993) and Chapman (1997) find unconditional nonlinear pricing kernels sufficient to capture the size effect alone, our results indicate that similar unconditional nonlinear pricing kernels considered here do not price the size and book-to-market effects simultaneously. However, nested model tests indicate that, in isolation, both conditioning information and nonlinearity significantly improve the pricing kernel performance for all five asset pricing models. The success of the conditional nonlinear FF3 model also suggests that the combination of conditioning and nonlinearity is critical to pricing kernel design. Implications for both academic researchers and practitioners are considered. / Business, Sauder School of / Finance, Division of / Graduate

This thesis consists of two essays which contribute to different but related aspects of the empirical asset pricing literature. The common theme is that incorrect restrictions can lead to inaccurate decisions. The first essay demonstrates that failure to account for the Federal Reserve experiment can lead to incorrect assumptions about the explosiveness of short-term interest rate volatility, while the second essay demonstrates that we need to incorporate skewness to develop models that adequately account for the cross-section of equity returns. Essay 1 empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the elasticity of volatility parameter for single-regime models unanimously indicate an explosive volatility process, whereas the Markov-switching models estimates are reasonable. We find that either Markov-switching or stochastic volatility, but not both, is needed to adequately fit the data. A robust conclusion is that volatility depends on the level of the short rate. Finally, the Markov-switching model is the best for forecasting. A technical contribution of this paper is a presentation of quasi-maximum likelihood estimation techniques for the Markov-switching stochastic-volatility model. Essay 2 proposes a new approach to estimating and testing nonlinear pricing models using GMM. The methodology extends the GMM based conditional mean-variance asset pricing tests of Harvey (1989) and He et al (1996) to include preferences over moments higher than variance. In particular we explore the empirical usefulness of the conditional coskewness of an assets return with the market return in explaining the cross-section of equity returns. The methodology is both flexible and parsimonious. We avoid modelling any asset specific parameters and avoid making restrictive assumptions on the dynamics of co-moments. By using GMM to estimate the models' parameters we also avoid making any assumptions about the distribution of the data. The empirical results indicate that coskewness is useful in explaining the cross-section of equity returns, and that both covariance and coskewness are time varying. We also find that the usefulness of coskewness is robust to the inclusion of Fama and French's (1993) SMB and HML factor returns. There is an interesting debate raging in the empirical asset pricing literature comparing the SDF versus beta methodologies. This paper's technique is a conditional version of the beta methodology, which turns out to be directly comparable with the SDF methodology with only minor modifications. Our SDF version imposes the CAPM's restrictions that the coefficients in the pricing kernel are known functions of the moments of market returns, which are modelled using macro-variables. We find that the SDF implied by the three-moment CAPM provides a better fit in this data set than current practice of parameterizing the coefficients on market returns in the SDF. This has an interesting application to the current SDF versus beta methodology debate. / Business, Sauder School of / Finance, Division of / Graduate

Different scenarios of the financial situation can take place before the company's bankruptcy. There may be long-term trends in the deteriorating financial situation that indicate the impending corporate bankruptcy, or the bankruptcy may occur unexpectedly, even though the company was ranked among prosperous business units. If the economic situation of the company followed the second scenario, when insolvency was quite predictable, static model, i.e. the model which does not take into account the dynamics of changes in the financial indicators, is a good option to capture the probability of bankruptcy. However, the situation becomes different when the financial indicators fail to show a positive trend throughout some years before the insolvency. In this case, the predictive accuracy of the static model could be increased by a dynamic model by taking into account the fact that the development of the financial indicators in the past periods may affect the company's financial health for the period under consideration.

The most effective and useful way to diagnose and subtype depression has been a long debated topic which even now does not have a definite answer. The biopsychosocial approach to diagnosis may be a solution to this problem by linking various etiologies to symptom presentation. The biopsychosocial model, in regard to depression, takes into account biological risk factors/contributors, psychological or cognitive risk factors/contributors, and social risk factors/contributors to depression when making diagnosis and subtyping determinations. However, the most effective way to use this model in the assessment, diagnosis, and treatment of depression is not yet clear. In this study, the utility of the biopsychosocial model as an effective approach to conceptualizing and treating depression was assessed by testing hypotheses that showed that etiological contributors are related to the presence and differential presentation of depression, and that these etiologically-based subtypes of depression respond differently to different forms of treatment. These hypotheses were tested using data from the National Comorbidity Survey - Replication (NCS-R). Results showed that the biopsychosocial model can effectively predict the presence, severity and chronicity of depression, and may inform specific biopsychosocially-based subtypes. No conclusions could be drawn regarding success in treatment based on the biopsychosocial model. Future directions for research based on the current study are discussed.

The Laskeek Bay Conservation Society has been collecting data on the East Limestone Island population of Ancient Murrelets since 1990. For the first 15 years of this decades-long study, mark-recapture data was collected annually on the adult population using one method while chicks were captured and tagged each year soon after their birth using another method. We developed a Jolly-Seber type model that integrates the adult and chick data. Using a multi-state framework, our model separates the `alive' state of the JS model into several age-related states, allowing for different survival and capture parameters between states. In the Ancient Murrelet case study we found that a 6-state model with constant adult survival and capture parameters best fit the data. We determined that, since the detected chicks are rarely seen again, including these individuals in the model does not result in estimates which greatly differ from that of the standard Jolly-Seber model but in a population in which juveniles have high survival and re-capture probabilities, the MSJS model is able to reduce the bias in estimates of population parameters. / Graduate

Energy-economy models are important tools used by policy-makers and researchers to design effective climate policy. However, there has been limited research that compares models against consistent characteristics to understand their impacts on climate policy projections. This can make it difficult for policy-makers to identify suitable models for their specific policy questions and develop effective climate policies. A web-based survey of energy-economy model users and developers in Canada’s public, private, and non-profit sectors (n=14) was conducted to systematically compare seventeen models against a framework of seven characteristics: technology characteristics, micro-, and macro-economic characteristics, policy representations, treatment of uncertainty, high-resolution spatial and temporal representations, and data transparency. It was found that for the most part, models represent technology, micro-, and macro-economic characteristics according to the classic typology of bottom-up, top-down, and hybrid models. However, our findings show that several modelling evolutions have occurred. Some top-down models can explicitly represent technologies and some bottom-up models incorporate microeconomic characteristics. Models differ in the types of policies they can simulate, sometimes underrepresenting performance regulations, government procurement, and research and development programs. All models incorporate at least one type of uncertainty analysis, models infrequently have high-resolution spatial and/or temporal representations, and most models lack publicly accessible methodological documents. Implications for researchers and policy-makers that use energy-economy models and/or develop policies are discussed. / Graduate