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Thematische Bibliographien / Evoluční návrh neuronové sítě

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Auswahl der wissenschaftlichen Literatur zum Thema „Evoluční návrh neuronové sítě“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Dissertationen zum Thema "Evoluční návrh neuronové sítě"

1

Piňos, Michal. „Evoluční návrh konvolučních neuronových sítí“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417210.

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The aim of this work is to design and implement a program for automated design of convolutional neural networks (CNN) with the use of evolutionary computing techniques. From a practical point of view, this approach reduces the requirements for the human factor in the design of CNN architectures, and thus eliminates the tedious and laborious process of manual design. This work utilizes a special form of genetic programming, called Cartesian genetic programming, which uses a graph representation for candidate solution encoding.This technique enables the user to parameterize the CNN search process and focus on architectures, that are interesting from the view of used computational units, accuracy or number of parameters. The proposed approach was tested on the standardized CIFAR-10dataset, which is often used by researchers to compare the performance of their CNNs. The performed experiments showed, that this approach has both research and practical potential and the implemented program opens up new possibilities in automated CNN design.

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2

Hytychová, Tereza. „Evoluční návrh neuronových sítí využívající generativní kódování“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445478.

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The aim of this work is to design and implement a method for the evolutionary design of neural networks with generative encoding. The proposed method is based on J. F. Miller's approach and uses a brain model that is gradually developed and which allows extraction of traditional neural networks. The development of the brain is controlled by programs created using cartesian genetic programming. The project was implemented in Python with the use of Numpy library. Experiments have shown that the proposed method is able to construct neural networks that achieve over 90 % accuracy on smaller datasets. The method is also able to develop neural networks capable of solving multiple problems at once while slightly reducing accuracy.

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3

Svobodová, Jitka. „Neuronové sítě a evoluční algoritmy“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218221.

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Objective of this master's thesis is optimizing of neral network topology using some of evolutionary algorithms. The backpropagation neural network was optimized using genetic algorithms, evolutionary programming and evolutionary strategies. The text contains an application in the Matlab environment which applies these methods to simple tasks as pattern recognition and function prediction. Created graphs of fitness and error functions are included as a result of this thesis.

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4

Mrnuštík, Michal. „Evoluční návrh využívající booleovské sítě“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237120.

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This master's thesis introduces the Random Boolean Networks as a developmental model in the evolutionary design. The representation of the Random Boolean Networks is described. This representation is combined with an evolutionary algorithm. The genetic operators are described too. The Random Boolean Networks are used as the developmental model for the evolutionary design of the combinational circuits and the sorting networks. Moreover a representation of the Random Boolean Networks for the design of image filters is introduced. The proposed methods are evaluated in different case-studies. The results of the experiments are discussed together with the potential improvements and topics of the next research.

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5

Pokorný, Petr. „Návrh síťového prvku pomocí neuronové sítě“. Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2008. http://www.nusl.cz/ntk/nusl-228226.

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The diploma thesis deal with a priority network switch whose model was made in programming environment Matlab - Simulink. Problem of optimal switching is solved by Hopfield’s artificial neural network. Produce of the diploma thesis is a model of packet switch and time-severity comparison of optimalization problem solved with or without artificial neural network. The thesis was developed in research project MSM 0021630529 Intelligent Systems in Automation.

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6

Karásek, Štěpán. „Neuronové sítě a genetické algoritmy“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255370.

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This thesis deals with evolutionary and genetic algorithms and the possible ways of combining them. The theoretical part of the thesis describes genetic algorithms and neural networks. In addition, the possible combinations and existing algorithms are presented. The practical part of this thesis describes the implementation of the algorithm NEAT and the experiments performed. A combination with differential evolution is proposed and tested. Lastly, NEAT is compared to the algorithms backpropagation (for feed-forward neural networks) and backpropagation through time (for recurrent neural networks), which are used for learning neural networks. Comparison is aimed at learning speed, network response quality and their dependence on network size.

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7

Stískal, Břetislav. „Návrh algoritmů pro neuronové sítě řídicí síťový prvek“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217527.

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This diploma thesis is devided into theoretic and practice parts. Theoretic part contains basic information about history and development of Artificial Neural Networks (ANN) from last century till present. Prove of the theoretic section is discussed in the practice part, for example learning, training each types of topology of artificial neural networks on some specifics works. Simulation of this networks and then describing results. Aim of thesis is simulation of the active networks element controlling by artificial neural networks. It means learning, training and simulation of designed neural network. This section contains algorithm of ports switching by address with Hopfield's networks, which used solution of typical Trade Salesman Problem (TSP). Next point is to sketch problems with optimalization and their solutions. Hopfield's topology is compared with Recurrent topology of neural networks (Elman's and Layer Recurrent's topology) their main differents, their advantages and disadvantages and supposed their solution of optimalization in controlling of network's switch. From thesis experience is introduced solution with controll function of ANN in active networks elements in the future.

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8

Roreček, Pavel. „Evoluční optimalizace konvolučních neuronových sítí“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385906.

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This Master's Thesis is focused on the principles of neural networks, primarily convolutional neural networks (CNN). It introduces the evolutionary optimization in the context of neural networks. One of existing libraries devoted to the CNN design was chosen (Keras), analysed and used in image classification tasks. An optimization technique based on cartesian genetic programming that should reduce the complexity of CNN's computation was proposed and implemented. The impact of the proposed technique on CNN behaviour was evaluated in a case study.

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9

Nétková, Barbora. „Evoluční návrh využívající přepisovací systémy“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255380.

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This master’s thesis proposes a method for the evolutionary design of rewriting systems. In particular, genetic algorithm will be applied to design rewriting rules for a specific variant of Lindenmayer system. The evolved rules of such grammar will be applied to generate growing sorting networks. Some distinct approaches to the rewriting process and construction of the sorting networks will be investigated. It will be shown that the evolution is able to successfully design rewriting rules for the proposed variants of rewriting processes. The results obtained exhibit abilities to successfully create partially growing sorting networks, which was evolved to grow for fewer inputs and in subsequent iterations grows up to 36 inputs.

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10

Šagát, Martin. „Návrh generativní kompetitivní neuronové sítě pro generování umělých EKG záznamů“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413114.

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The work deals with the generation of ECG signals using generative adversarial networks (GAN). It examines in detail the basics of artificial neural networks and the principles of their operation. It theoretically describes the use and operation and the most common types of failures of generative adversarial networks. In this work, a general procedure of signal preprocessing suitable for GAN training was derived, which was used to compile a database. In this work, a total of 3 different GAN models were designed and implemented. The results of the models were visually displayed and analyzed in detail. Finally, the work comments on the achieved results and suggests further research direction of methods dealing with the generation of ECG signals.

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