Thesis (M. Sc. (Computer Science))--University of Pretoria, 2000. / Summaries in Afrikaans and English. Includes bibliographical references.
The principal problem is the control of a nonlinear system with uncertainty. We will consider a robot manipulator system, which is nonlinear, and uncertain (unknown parameters and modeling errors). Our goals are to come up with a design of controllers that insure the stability of the system and provide robustness to parameters changes and modeling errors. We will use the theory developed for uncertain linear systems after carrying out an exact linearization of the original system. This linearization which is not an approximation, has been recently developed. The linear part of the controller has been designed so as to guarantee tracking and disturbance rejection. However, additional constraints resulting from the original nonlinear system have to be taken care of. Our design is tested by simulation on a two degree of freedom robot manipulator, which is simple enough to simulate but has all the properties of more general manipulators.
Retraining neural networks for the prediction of Dst in the Rice magnetospheric specification and forecast modelCostello, Kirt Allen January 1996 (has links)
Artificial Neural Networks have been developed at Rice University for the forecasting of the Dst index from solar wind and Dst parameters. The one hour Dst index is an Earth based measurement of variations in the H-component of the magnetic field that is indicative of the strength of the ring current, and thus magnetic storms. Comparison of the neural networks' outputs to the OMNI dataset values of Dst will be presented. These results verify the success of the neural networks in predicting Dst. Network performance when predicting Dst two or more hours into the future and testing of MSFM output based on neural net Dst input for the August 1990 storm will be presented. Comparisons between MSFM equatorial particle fluxes and CRRES satellite observations show the MSFM 10 keV proton equatorial fluxes raise interesting questions about the MSFM's use of the Dst input parameter.
Chen, Hsinchun, Lynch, K.J., Basu, K., Ng, Tobun Dorbin
Artificial Intelligence Lab, Department of MIS, University of Arizona / This Blackboard-based design uses a neural-net spreading-activation algorithm to traverse multiple thesauri. Guided by heuristics, the algorithm activates related terms in the thesauri and converges on the most pertinent concepts.
Chen, Hsinchun, Buntin, P., She, Linlin, Sutjahjo, S., Sommer, C., Neely, D.
Artificial Intelligence Lab, Department of MIS, University of Arizona / For our research, we investigated a different problem-solving scenario called game playing, which is unstructured, complex, and seldom-studied. We considered several real-life game-playing scenarios and decided on greyhound racing. The large amount of historical information involved in the search poses a challenge for both human experts and machine-learning algorithms. The questions then become: Can machine-learning techniques reduce the uncertainty in a complex game-playing scenario? Can these methods outperform human experts in prediction? Our research sought to answer these questions.
Chen, Hsinchun, Nunamaker, Jay F., Orwig, Richard E., Titkova, Olga
Artificial Intelligence Lab, Department of MIS, University of Arizona / A prototype tool classifies output from an electronic meeting system into a manageable list of concepts, topics, or issues that a group can further evaluate. In an experiment with output from GroupSystems electronic meeting system, the tool's recall ability was comparable to that of a human facilitator, but took roughly a sixth of the time.
Bailey, Charles W.
A multimedia computer system is one that can create, import, integrate, store, retrieve, edit, and delete two or more types of media materials in digital form, such as audio, image, full-motion video, and text information. This paper surveys four possible types of multimedia computer systems: hypermedia, multimedia database, multimedia message, and virtual reality systems. The primary focus is on advanced multimedia systems development projects and theoretical efforts that suggest long-term trends in this increasingly important area.
Estimating drug/plasma concentration levels by applying neural networks to pharmacokinetic data setsTolle, Kristin M., Chen, Hsinchun, Chow, Hsiao-Hui January 2000 (has links)
Artificial Intelligence Lab, Department of MIS, University of Arizona / Predicting blood concentration levels of pharmaceutical agents in human subjects can be made difficult by missing data and variability within and between human subjects. Biometricians use a variety of software tools to analyze pharmacokinetic information in order to conduct research about a pharmaceutical agent. This paper is the comparison between using a feedforward backpropagation neural network to predict blood serum concentration levels of the drug tobramycin in pediatric cystic fibrosis and hemotologicâ oncologic disorder patients with the most commonly used software for analysis of pharmacokinetics, NONMEM. Mean squared standard error is used to establish the comparability of the two estimation methods. The motivation for this research is the desire to provide clinicians and pharmaceutical researchers a cost effective, user friendly, and timely analysis tool for effectively predicting blood concentration ranges in human subjects.
The Intelligent Reference Information System Project: A Merger of CD-ROM LAN and Expert System TechnologiesBailey, Charles W. January 1992 (has links)
The University Libraries of the University of Houston created an experimental Intelligent Reference Information System (IRIS) over a two-year period. A ten-workstation CD-ROM LAN was implemented that provided access to nineteen citation, full-text, graphic, and numeric databases. An expert system, Reference Expert, was developed to assist users in selecting appropriate printed and electronic reference sources. This expert system was made available on both network and stand-alone workstations. Three research studies were conducted.
Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networksXu, Jennifer J., Chen, Hsinchun January 2004 (has links)
Artificial Intelligence Lab, Department of MIS, University of Arizona / Effective and efficient link analysis techniques are needed to help law enforcement and intelligence agencies fight organized crimes such as narcotics violation, terrorism, and kidnapping. In this paper, we propose a link analysis technique that uses shortest-path algorithms, priority-first-search (PFS) and two-tree PFS, to identify the strongest association paths between entities in a criminal network. To evaluate effectiveness, we compared the PFS algorithms with crime investigatorsâ typical association-search approach, as represented by a modified breadth-first-search (BFS). Our domain expert considered the association paths identified by PFS algorithms to be useful about 70% of the time, whereas the modified BFS algorithmâ s precision rates were only 30% for a kidnapping network and 16.7% for a narcotics network. Efficiency of the two-tree PFS was better for a small, dense kidnapping network, and the PFS was better for the large, sparse narcotics network.
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