|
|
Evolutionary Optimization of Convolutional Neural Networks
Čoupek, Vojtěch ; Mrázek, Vojtěch (referee) ; Sekanina, Lukáš (advisor)
This thesis deals with the problem of neural network weights compression using the technique of Weight-Sharing and parameter optimization of this technique by unconventional optimization algorithms. The reason for the optimization is decreasing the memory or energy demands of the neural network response calculation. The aim is to design a system that accepts a neural network and reduces its memory demands. Its functionality is demonstrated with the help of several experiments. The thesis investigates the use of various optimization algorithms, additional compression using the quantization above the Weight-Sharing technique, and proposes the quantization results tuning method to improve accuracy. These procedures are first tested on the Le-Net-5 network and then applied for the MobileNet\_v2. network compression.
|
|
|
Robotic Neurosurgery Planning Tool
Sadleková, Simona ; Olivová, Jana (referee) ; Kadlec, Petr (advisor)
Táto diplomová práca sa zaoberá problematikou plánovania robotických operácií pre neurochirurgiu a optimalizačnými algoritmami používanými na návrh operačných zákrokov nimi prevádzaných, predovšetkým algoritmom VNDMOPSO. V úvodnej kapitole je vysvetlený základný princíp prevádzania robotických operácií a popísaný koncentrický trubicový robot používaný pre tento účel. Ďalej sa práca zaoberá všeobecným popisom optimalizačných problémov a algoritmov ich riešiacich a následne je podrobne popisovaný algoritmus VNDMOPSO, vybraný pre daný optimalizačný problém. Pre tento algoritmus je vytvorená v prostredí MATLAB funkcia, ktorá je následne testovaná na viacerých testovacích úlohách. V nasledujúcich častiach je overovaná jej funkčnosť na reálnych tvaroch nádorov a je predstavené grafické užívateľské prostredie, ktoré slúži ako nástroj pre plánovanie neurochirurgických robotických operácií. V záverečnej časti práce je vyhodnotený vplyv nastavenia jednotlivých parametrov algoritmu na výsledky optimalizácie.
|
|
| |
|
|
The Role of Advanced Option Pricing Techniques Empirical Tests on Neural Networks
Brejcha, Jiří ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
This thesis concerns with a comparison of two advanced option-pricing techniques applied on European-style DAX index options. Specifically, the study examines the performance of both the stochastic volatility model based on asymmetric nonlinear GARCH, which was proposed by Heston and Nandi (2000), and the artificial neural network, where the conventional Black-Scholes-Merton model serves as a benchmark. These option-pricing models are tested with the use of the dataset covering the period 3rd July 2006 - 30th October 2009 as well as of its two subsets labelled as "before crisis" and "in crisis" data where the breakthrough day is the 17th March 2008. Finding the most appropriate option-pricing method for the whole periods as well as for both the "before crisis" and the "in crisis" datasets is the main focus of this work. The first two chapters introduce core issues involved in option pricing, while the subsequent third section provides a theoretical background related to all of above-mentioned pricing methods. At the same time, the reader is provided with an overview of the theoretical frameworks of various nonlinear optimization techniques, i.e. descent gradient, quassi-Newton method, Backpropagation and Levenberg-Marquardt algorithm. The empirical part of the thesis then shows that none of the...
|
|
|
On the predictibility of Central European stock returns: Do Neural Networks outperform modern economic techniques?
Baruník, Jozef ; Žikeš, Filip (advisor) ; Vošvrda, Miloslav (referee)
In this thesis we apply neural networks as nonparametric and nonlinear methods to the Central European stock markets returns (Czech, Polish, Hungarian and German) modelling. In the first two chapters we define prediction task and link the classical econometric analysis to neural networks. We also present optimization methods which will be used in the tests, conjugate gradient, Levenberg-Marquardt, and evolutionary search method. Further on, we present statistical methods for comparing the predictive accuracy of the non-nested models, as well as economic significance measures. In the empirical tests we first show the power of neural networks on Mackey-Glass chaotic time series followed by real-world data of the daily and weekly returns of mentioned stock exchanges for the 2000:2006 period. We find neural networks to have significantly lower prediction error than classical models for daily DAX series, weekly PX50 and BUX series. The lags of time-series were used, and also cross-country predictability has been tested, but the results were not significantly different. We also achieved economic significance of predictions with both daily and weekly PX-50, BUX and DAX with 60% accuracy of prediction. Finally we use neural network to learn Black-Scholes model and compared the pricing errors of...
|
|
|
On the predictability of Central European stock returns : "Do neural networks outperform modern econometric techniques?"
Baruník, Jozef ; Schneider, Ondřej (advisor)
In this thesis we apply neural networks as nonparametric and nonlinear methods to the Central European stock markets returns (Czech, Polish, Hungarian and German) modelling. In the first two chapters we define prediction task and link the classical econometric analysis to neural networks. We also present optimization methods which will be used in the tests, conjugate gradient, Levenberg-Marquardt, and evolutionary search method. Further on, we present statistical methods for comparing the predictive accuracy of the non-nested models, as well as economic significance measures. In the empirical tests we first show the power of neural networks on Mackey-Glass chaotic time series followed by real-world data of the daily and weekly returns of mentioned stock exchanges for the 2000:2006 period. We find neural networks to have significantly lower prediction error than classical models for daily DAX series, weekly PX50 and BUX series. The lags of time-series were used, and also cross-country predictability has been tested, but the results were not significantly different. We also achieved economic significance of predictions with both daily and weekly PX-50, BUX and DAX with 60% accuracy of prediction. Finally we use neural network to learn Black-Scholes model and compared the pricing errors of Black-Scholes and...
|
|
|
Overview of Actual Approaches to Optimization
Hudecová, Patrícia ; Šůstek, Martin (referee) ; Zbořil, František (advisor)
This work aimed to study some of the optimization algorithms inspired by nature and to test their success in finding the extreme of a function on various functions. Four algorithms were selected, namely the bat algorithm, the firefly algorithm, the flower pollination algorithm, and the black hole algorithm. The Griewank function, the Rastringin function, and the Rosenbrock function were chosen as test functions for finding the extreme of the function. The work contains a description of individual algorithms, a description of test functions and a description of the experiments, and an evaluation of the success of the algorithms.
|
|
|
Optimization Algorithms Inspired by Nature
Babjarčiková, Lenka ; Zbořil, František (referee) ; Zbořil, František (advisor)
This thesis deals with four optimization algorithms inspired by nature. It describes ant colony optimization algorithm, marriage in honeybees optimization algorithm, grey wolf optimization algorithm and simulated annealing algorithm. The main part of this thesis is the application of these algorithms for solving three optimization problems. One of the problems is travelling salesman problem, which is solved by ant colony optimization, next problem is searching for extreme of function solved by grey wolf optimization and simulated annealing algorithms and the last is boolean satisfiability problem solved by marriage in honeybees optimization algorithm. Thesis contains experiments with these algorithms and reviews gained results.
|
|
|
On the predictability of Central European stock returns : "Do neural networks outperform modern econometric techniques?"
Baruník, Jozef ; Schneider, Ondřej (advisor)
In this thesis we apply neural networks as nonparametric and nonlinear methods to the Central European stock markets returns (Czech, Polish, Hungarian and German) modelling. In the first two chapters we define prediction task and link the classical econometric analysis to neural networks. We also present optimization methods which will be used in the tests, conjugate gradient, Levenberg-Marquardt, and evolutionary search method. Further on, we present statistical methods for comparing the predictive accuracy of the non-nested models, as well as economic significance measures. In the empirical tests we first show the power of neural networks on Mackey-Glass chaotic time series followed by real-world data of the daily and weekly returns of mentioned stock exchanges for the 2000:2006 period. We find neural networks to have significantly lower prediction error than classical models for daily DAX series, weekly PX50 and BUX series. The lags of time-series were used, and also cross-country predictability has been tested, but the results were not significantly different. We also achieved economic significance of predictions with both daily and weekly PX-50, BUX and DAX with 60% accuracy of prediction. Finally we use neural network to learn Black-Scholes model and compared the pricing errors of Black-Scholes and...
|
|
|
Video stabilization using global optimization algorithms
Bartoš, Patrik ; Říha, Kamil (referee) ; Kříž, Petr (advisor)
This bachelor thesis focuses on video stabilization using CRS (Controlled Random Search) and GA (Genetic Algorithm) optimization algorithms. It describes registration process, geometrical transformations, interpolation methods, similarity criteria and optimization algorithms. It also briefly describes structure of the program created in MATLAB. Finally it contains results of achieved stabilization.
|
guest ::
