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.
01 January 2011
This thesis is concerned with sequential decision making by multiple agents, whether they are acting cooperatively to maximize team reward or selfishly trying to maximize their individual rewards. The practical intractability of this general problem led to efforts in identifying special cases that admit efficient computation, yet still represent a wide enough range of problems. In our work, we identify the class of problems with structured interactions, where actions of one agent can have non-local effects on the transitions and/or rewards of another agent. We addressed the following research questions: (1) How can we compactly represent this class of problems? (2) How can we efficiently calculate agent policies that maximize team reward (for cooperative agents) or achieve equilibrium (self-interested agents)? (3) How can we exploit structured interactions to make reasoning about communication offline tractable? ^ For representing our class of problems, we developed a new decision-theoretic model, Event-Driven Interactions with Complex Rewards (EDI-CR), that explicitly represents structured interactions. EDI-CR is a compact yet general representation capable of capturing problems where the degree of coupling among agents ranges from complete independence to complete dependence.^ For calculating agent policies, we draw on several techniques from the field of mathematical optimization and adapt them to exploit the special structure in EDI-CR. We developed a Mixed Integer Linear Program formulation of EDI-CR with cooperative agents that results in programs much more compact and faster to solve than formulations ignoring structure. We also investigated the use of homotopy methods as an optimization technique, as well as formulation of self-interested EDI-CR as a system of non-linear equations.^ We looked at the issue of communication in both cooperative and self-interested settings. For the cooperative setting, we developed heuristics that assess the impact of potential communication points and add the ones with highest impact to the agents’ decision problems. Our heuristics successfully pick communication points that improve team reward while keeping problem size manageable. Also, by controlling the amount of communication introduced by a heuristic, our approach allows us to control the tradeoff between solution quality and problem size.^ For self-interested agents, we look at an example setting where communication is an integral part of problem solving, but where the self-interested agents have a reason to be reticent (e.g. privacy concerns). We formulate this problem as a game of incomplete information and present a general algorithm for calculating approximate equilibrium profile in this class of games.^
Thesis--University of Wisconsin--Madison. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 159-164).
Thesis (Ph.D.)--Lehigh University, 2007.
(has links) (PDF)
Thesis (M.S. in Modeling, Virtual Environments, and Simulation (MOVES))--Naval Postgraduate School, March 2008. / Thesis Advisor(s): Darken, Christian. "March 2008." Description based on title screen as viewed on May 21, 2008. Includes bibliographical references (p. 47-49). Also available in print.
Koperski, Jeffrey David,
Thesis (M.A.)--Liberty University Graduate School of Religion, 1991. / Includes bibliographical references.
Roth, Gerhard, Carleton University. Dissertation. Computer Science.
Thesis (M.C.S.)--Carleton University, 1985. / Also available in electronic format on the Internet.
Johansson, Stefan J.
Diss. Ronneby : Tekn. högsk., 2002.
Page generated in 0.1202 seconds