Global ETD Search

1	Generic simulation modelling of stochastic continuous systems Albertyn, Martin. January 2004 (has links) Thesis (Ph.D.)(Industrial and Systems Eng.)--University of Pretoria, 2004. / Includes bibliographical references. Probabilistic automata.
2	Calibration and use of expert probability judgements Wiper, Michael Peter January 1990 (has links) No description available. 510 Probabilistic forecasting
3	Style classification of cursive script recognition Dehkordi, Mandana Ebadian January 2003 (has links) No description available. 006 Probabilistic neural network
4	Models of programs and machine learning Bone, Nicholas January 1998 (has links) No description available. 005 Probabilistic programs
5	Representation, learning, description and criticism of probabilistic models with applications to networks, functions and relational data Lloyd, James Robert January 2015 (has links) No description available. 620 Probabilistic number theory
6	A Probabilistic Approach for ATC Calculation Tsai, Chih-Yi 14 June 2000 (has links) Because of the essentially stochastic nature of power systems behavior, mainly due to random equipment outages and load variations, it is very important to consider uncertainties of power systems to promote the accuracy of Available Transfer Capability (ATC) calculations. This paper proposed a probabilistic technique "Bootstrap" to provide an accuracy criteria for ATC estimation that uses historical data and a future postulated condition. The bootstrap is a computer-based method for assigning measures of accuracy to statistical estimates. Test results showed that when the uncertainties of loads and line outages were considered, using the proposed probabilistic approach the confidence intervals could reflect the accuracy of the posted ATC values and it will be more flexible in bidding ATC to further aid transmission customers in their evaluation of transaction biddings and risk analysis. probabilistic technique uncertainty
7	The Pythagorean random variable Heckmann, Gary Allan, 1945- January 1972 (has links) No description available. Probabilistic number theory.
8	Probabilistic and Stochastic Computational Models: from Nanoelectronic to Biological Applications Liang, Jinghang Unknown Date No description available. stochastic computation probabilistic computation
9	Combination of multiple feature streams for robust speech recognition Jancovic, Peter January 2002 (has links) No description available. 006 Probabilistic union models
10	Probabilistic decoupling for dynamic multi-variable stochastic systems Zhang, Qichun January 2016 (has links) Decoupling control is widely applied to multi-input multi-output industrial processes. The traditional decoupling control methods are based on accurate models, however it is difficult or impossible to obtain accurate models in practice. Moreover, the traditional decoupling control methods are not suitable for the analysis of the couplings among system outputs which are subjected to the random noises. To solve the problems mentioned above, we will look into the decoupling control problem in probability sense. To describe this control problem, probabilistic decoupling has been presented as a novel concept based on statistical independence. Using probability theory, a set of new control objectives has been extended by this presented concept. Conditions of probabilistic complete decoupling are given. Meanwhile, the relationship between the traditional decoupling and probabilistic decoupling has been analyzed in this thesis, theoretically. To achieve the control objectives of probabilistic decoupling, various control algorithms are developed for dynamic multi-variable stochastic systems, which are represented by linear stochastic models, bilinear stochastic models and stochastic nonlinear models, respectively. For linear stochastic models subjected to Gaussian noises, the covariance control theory has been used. The Output-feedback stabilization via block backstepping design has been considered for bilinear stochastic systems subjected to Gaussian noises. Furthermore, the minimum mutual information control has been proposed for stochastic nonlinear systems subjected to non-Gaussian noises. Some advanced topics are also considered in this thesis. The stochastic feedback linearization can be applied to a class of stochastic nonlinear systems and the reduced-order closed-form covariance control models are also presented, which can be applied in covariance control theory. Using kernel density estimation, data-based minimum mutual information control is given to extend the presented minimum mutual information control algorithm.

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