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11	Analýza genetických algoritmů / Analysis of Genetic Algorithm Snášelová, Petra January 2013 (has links) This thesis deals with analysis of genetic algorithms. It is focused on various approaches to creation of new populations. A comparison between basic principles of operation of genetic algorithms and processes occurring in living organisms is drawn here. Some methods of application of particular steps of genetic algorithms are introduced and a suitability of the methods to certain types of problems is considered. The main goal in the thesis is to apply genetic algorithms in solving three types of optimization problems, namely the solution of functions with a single major extreme, functions with flat (slight) extreme and also functions with many local extremes.
12	A Bayesian Decision Theoretical Approach to Supervised Learning, Selective Sampling, and Empirical Function Optimization Carroll, James Lamond 10 March 2010 (has links) (PDF) Many have used the principles of statistics and Bayesian decision theory to model specific learning problems. It is less common to see models of the processes of learning in general. One exception is the model of the supervised learning process known as the "Extended Bayesian Formalism" or EBF. This model is descriptive, in that it can describe and compare learning algorithms. Thus the EBF is capable of modeling both effective and ineffective learning algorithms. We extend the EBF to model un-supervised learning, semi-supervised learning, supervised learning, and empirical function optimization. We also generalize the utility model of the EBF to deal with non-deterministic outcomes, and with utility functions other than 0-1 loss. Finally, we modify the EBF to create a "prescriptive" learning model, meaning that, instead of describing existing algorithms, our model defines how learning should optimally take place. We call the resulting model the Unified Bayesian Decision Theoretical Model, or the UBDTM. WE show that this model can serve as a cohesive theory and framework in which a broad range of questions can be analyzed and studied. Such a broadly applicable unified theoretical framework is one of the major missing ingredients of machine learning theory. Using the UBDTM, we concentrate on supervised learning and empirical function optimization. We then use the UBDTM to reanalyze many important theoretical issues in Machine Learning, including No-Free-Lunch, utility implications, and active learning. We also point forward to future directions for using the UBDTM to model learnability, sample complexity, and ensembles. We also provide practical applications of the UBDTM by using the model to train a Bayesian variation to the CMAC supervised learner in closed form, to perform a practical empirical function optimization task, and as part of the guiding principles behind an ongoing project to create an electronic and print corpus of tagged ancient Syriac texts using active learning. machine learning supervised learning function optimization empirical function optimization statistics Bayes Bayes Law Bayesian Bayesian learning decision theory utility theory unified Bayesian model UBM UBDTM learning framework no-free-lunch NFL a priori learning extended Bayesian formalism EBF bias inductive bias hypothesis space function class active learning uncertainty sampling query by uncertainty query by committee expected value of sample information EVSI Computer Sciences
13	Kennwertorientierte Methoden zur Auswahl und Synthese von Bewegungsgesetzen für Motion-Control-Systeme Speicher, Thorsten 04 October 2021 (has links) Im Rahmen dieser Arbeit werden Methoden präsentiert, die Anwender bei der Auslegung von taktzeitoptimalen Bewegungen für typische Rast-in-Rast-Charakteristiken unter Verwendung von normierten Bewegungsgesetzen nach der VDI-Richtlinie 2143 unterstützen. Es wird eine Herangehensweise vorgestellt, die bei Vorgabe von kinematischen Bewegungsgrenzen zu verwenden ist. Führt dabei die resultierende Bewegung zu einer unzulässigen Erwärmung des Antriebs, lässt sich mit Hilfe anderer Werkzeuge die minimal mögliche und somit optimale Taktzeit effizient bestimmen.:1 Einleitung 2 Stand der Forschung 3 Grundlagen 4 Taktzeitoptimierung bei kinematischen Restriktionen 5 Taktzeitoptimierung bei thermischen Restriktionen 6 Anwendungsbeispiele 7 Zusammenfassung und Ausblick / This paper presents methods to help users design motions with ideal cycle times for rest in rest applications by using the normalized laws of motion according to the VDI guideline 2143. The approach shown is used for systems that are limited by their kinematic parameters. If the resulting movement leads to inadmissible heating of the drive, other tools are able to determine the minimal cycle time considering the thermal limit.:1 Einleitung 2 Stand der Forschung 3 Grundlagen 4 Taktzeitoptimierung bei kinematischen Restriktionen 5 Taktzeitoptimierung bei thermischen Restriktionen 6 Anwendungsbeispiele 7 Zusammenfassung und Ausblick info:eu-repo/classification/ddc/620 ddc:620

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