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MARKETING MIX OF FIT BOX KLADNO
Šíma, Jiří ; Omcirk, Vilém (advisor) ; Pecinová, Markéta (referee)
Title: MARKETING MIX OF FIT BOX KLADNO Aims: The aim of this thesis is to collect information on the operation of the marketing mix from the customers and managers point of view. Based on these findings new proposal and recommendations were suggested in order to improve existing marketing mix. Methods: The analysis of marketing mix was performed by marketing research through questionnaire, interview and observation. Results: According to the results customers are least satisfied with changing rooms and web sites. In the contrary, most customers are satisfied with lessons quality and instructors attitude. Keywords: marketing mix, fit box, questionnaire, interview
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The Computational Power of Neural Networks and Representations of Numbers in Non-Integer Bases
Šíma, Jiří
We briefly survey the basic concepts and results concerning the computational power of neural networks which basically depends on the information content of weight parameters. In particular, recurrent neural networks with integer, rational, and arbitrary real weights are classified within the Chomsky and finer complexity hierarchies. Then we refine the analysis between integer and rational weights by investigating an intermediate model of integer-weight neural networks with an extra analog rational-weight neuron (1ANN). We show a representation theorem which characterizes the classification problems solvable by 1ANNs, by using so-called cut languages. Our analysis reveals an interesting link to an active research field on non-standard positional numeral systems with non-integer bases. Within this framework, we introduce a new concept of quasi-periodic numbers which is used to classify the computational power of 1ANNs within the Chomsky hierarchy.
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Gradient learning for networks of smoothly pulse neurons
Hošek, Lukáš ; Šíma, Jiří (advisor) ; Petříčková, Zuzana (referee)
Networks of spiking neurons present a biologically more plausible alternative to perceptron networks, having great potential for processing time series. However, as of now, no practically usable learning algorithm has been known. SpikeProp, based on a gradient descent method, and its modifications have a fundamental problem with dis-continuity of spike creation and deletion. A new nontrivial gradient learning algorithm for a model of smoothly spiking neurons is proposed as a possible way to solve this problem. The goal of this work is to implement and test this model and eventually propose further improvements.
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Optimizing of air distribution in schools
Cigánková, Kristýna ; Šíma, Jiří (referee) ; Šikula, Ondřej (advisor)
The thesis deals with the issue of indoor air quality of schools and kindergartens. It focuses particularly on the inadequate ventilation and the application of forced ventila-tion in these types of buildings. The proposed solution is applied to a kindergarten in Kuřim. Measurements of the C02 concentration levels were performed in the presence and absence of air conditioning. To elaborate the proposed solution a simulation was made using a software called ANSYS Fluent. Input values for the simulation were obtained from an experimental measurement of a ventilation diffuser made by a company called Climecon ROX using the PIV method. This diffuser was then installed in the kindergarten. The measure-ments were carried out in the framework of the project the experimental validation of numerical models of the air flow in buildings marked with FAST-S-6-3387.
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Cut Languages in Rational Bases
Šíma, Jiří ; Savický, Petr
We introduce a so-called cut language which contains the representations of numbers in a rational base that are less than a given threshold. The cut languages can be used to refine the analysis of neural net models between integer and rational weights. We prove a necessary and sufficient condition when a cut language is regular, which is based on the concept of a quasi-periodic power series. We show that any cut language with a rational threshold is context-sensitive while examples of non-context-free cut languages are presented.
Fulltext: content.csg -  PDF
Plný tet: v1236-16 - PDF
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Neural Networks Between Integer and Rational Weights
Šíma, Jiří
The analysis of the computational power of neural networks with the weight parameters between integer and rational numbers is refined. We study an intermediate model of binary-state neural networks with integer weights, corresponding to finite automata, which is extended with an extra analog unit with rational weights, as already two additional analog units allow for Turing universality. We characterize the languages that are accepted by this model in terms of so-called cut languages which are combined in a certain way by usual string operations. We employ this characterization for proving that the languages accepted by neural networks with an analog unit are context-sensitive and we present an explicit example of such non-context-free languages. In addition, we formulate a sufficient condition when these networks accept only regular languages in terms of quasi-periodicity of parameters derived from their weights.
Fulltext: content.csg - PDF
Plný tet: v1237-16 - PDF
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The compensation forms of stress of executives
ŠÍMA, Jiří
The Bachelor Thesis deals with the issueof stress in managersand with the way managers compensate their stress. This Bachelor thesis concentrated the theoretical part on the causes, clasification, signs, consequences and compensation of stress with focus on work stress related to managers
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