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Computational modelling of thermal behavior of DC motor
Grepl, Robert ; Wierciak, J. ; Vlach, R.
The paper deals with computational modelling of thermal behavior ofdrive system with DC motor. Motivation to built dynamical thermal model is toincrease utility value performance of electrical motor in particular roboticapplication. There is mathematical model presented in the paper, describedidentification of parameters process and shown results for two kind ofidentification approaches. Usage of neural networks approximators ismentioned.
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Use of Neural Networks in Equity Trading
Lahodová, Martina ; Veselá, Jitka (advisor) ; Stádník, Bohumil (referee)
The neural networks have been the fastest developing area of computer science lately. They have strong interdisciplinary character, so that they can be applied in many fields of human activity e.g. capital markets. The objective of the thesis is to apply a perceptron model to predicting future value of a sample of shares, to set the accuracy of prediction and bring the conclusion of reliable use of the neural networks. Opening chapters are concerned with general principles of neural networks functioning, their classification and different ways of their "learning". Analytic chapter is based on the creation and use of the perceptron model and the analysis of given results.
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Applications of neural networks and Elliot´s waves on selected shares
Polaková, Soňa ; Veselá, Jitka (advisor) ; Musílek, Petr (referee)
Using modern methods of share quotations forecasting is the main goal of this thesis. The special accent is placed on forecasting the trend by means of artificial neural network especially on the optimalization of variables in the training process. Elliot's wave theory is applied in the second part of the thesis, particularly on prediction of future share quotation progress. Buying or selling signal generated by these two methods is consequently compared with ex-post signal yielding a profit. Lastly, successfulness of using these methods for forecasting at stock market is evaluated.
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Analysis of Stock Exchange Data to UI Methods
Kutina, Michal ; Jelínek, Jiří (advisor) ; Dvořák, Pavel (referee)
The graduation thesis "Analysis of stock-exchange data using AI methods" is focused on the use of neural networks while predicating the exchange-rate movements on Change. The theoretical part is divided into three independent units. The Change matters and the related individual terms are described in the first part. In the second part, the two basic approaches to the stock-exchange data analysis are analyzed, these two approaches being the fundamental and technical analysis. The third, and the last, theoretical part forms an individual unit describing the Artificial Intelligence theory. Particularly the issue of the neuronal networks is described in detail. The practical part seeks the use for the chosen neuronal network GAME. It analyses the chosen YMZ9 market. It focuses on the prediction of the exchange-rate movements using the "sliding window" method. The last chapter summarizes the results and it proves that under certain circumstances it is possible to properly use the neuronal networks both for the prediction of the stock-exchange movements and as one of the corner-stones of the profitable trading system.
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History and Development of Artificial Inteligence
Kraitz, Petr ; Jirků, Petr (advisor) ; Berka, Petr (referee)
This work describes relatively short history of the field of Artificial Intelligence. It contains main obstacles and challenges, which scientists in the past and present solved and still have to solve, as well as a description of methods how to deal with those challenges and which possibilities are there for the future development of the field. The main focus of the work was not to present a full picture of the field of Artificial Intelligence, but to introduce this interesting field, its founders and nowadays scientists, main problems and related disciplines of science. The work is divided into chapters describing the history of the field artificial intelligence in the last century and related fields of informatics, biology, psychology, economy and hardware technology. Next chapter presents to the reader the main problems of the field, which were and still are being solved in the top research and development laboratories in the world. Next two chapters introduce two approaches to dealing with said problems, namely neural networks and evolutional algorithms and expert systems as an example of the oldest application of Artificial Intelligence in "Good Old-fashioned Artificial Intelligence" way as defined by John Haugeland. The last chapter is just an outline how the next few years in the filed may look like and what can we await in the next years.
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Economical models realized by neural network GMDH type
Beneš, Vratislav ; Jablonský, Josef (advisor) ; Hrabčák, Petr (referee)
This diploma thesis is about design and realization of neural network MIA GMDH for ekonomical modelling by inductive method. Models are compared with statistical methods by quallity and usebility degree. An application was developed for verification of functionality on experiments. The same experiments were run in econometrical software. The results were compared. The MIA GMDH is suitable for economic modelling.
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Rozhodování člověka a počítače
Belák, Václav ; Rosický, Antonín (advisor) ; Havel, Ivan (referee) ; doc. Ing. Ivan M. Havel, CSc. Ph.D., (referee)
Rozhodování je procesem výběru jedné z možných variant akcí. U jedince se jedná o komplexní proces, který je ovlivněn očekáváním jedince, jeho znalostmi, porozuměním světu, hodnotami a také jeho schopností tvořit. Komplexita tohoto procesu vzrůstá, uvážíme-li postavení jedince v kolektivu. Vyrovnávání se s těmito problémy je tradičně výzvou pro psychologii a management. Autorovým cílem je pojmutí této problematiky z perspektivy kognitivní vědy spolu s akcentem na inteligentní systémy pro podporu rozhodování. Nastíníme některé ze základních principů těchto systémů a po pojmenování rozdílů mezi těmito a lidským procesem rozhodování se přesuneme k jednotlivým přínosům a úskalím kombinace obou procesů rozhodování, v jejichž vhodné kombinaci spočívá úspěch budoucích rozhodnutí.
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Modern Methods for Exchange Rete Prediction
Buryan, Petr ; Taušer, Josef (advisor)
Tato práce se snaží nabídnout odpověď na otázku, zda má smysl při rozhodování o budoucím pohybu měnových kurzů brát ohled na výsledky vystupující z modelů získaných analýzou měnových kurzů a relevantních časových řad provedeného pomocí metod strojového učení. Účelem této práce je tak prozkoumat možnosti analýzy kurzů (ve formě časových řad) s důrazem na použití nových metod spočívajících svým těžištěm v oblasti umělé inteligence a strojového učení (neuronové sítě, algoritmus GMDH sítí).
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