National Repository of Grey Literature 8 records found  Search took 0.00 seconds. 
Balancing Exploitation and Exploration via Fully Probabilistic Design of Decision Policies
Kárný, Miroslav ; Hůla, František
Adaptive decision making learns an environment model serving a design of a decision policy. The policy-generated actions influence both the acquired reward and the future knowledge. The optimal policy properly balances exploitation with exploration. The inherent dimensionality\ncurse of decision making under incomplete knowledge prevents the realisation of the optimal design.
Equalization of the transmission channel
Žlebek, Lukáš ; Šilhavý, Pavel (referee) ; Číž, Radim (advisor)
This thesis describes a design of a simulation of transmission of digital information via communication system and equalization of communication function. The layout of communication channel with multiway transmission is described in following part. Next part is about hardware modulator which generate modulated signal which is transmitted via communication channel and after is sampled by A/D convertion card to computer, where is equalizated and demodulated in Simulink. In the last part of this thesis, there is proposal of the laboratory task and its sample solution.
Performance based adaptation of Scala programs
Kubát, Petr ; Bureš, Tomáš (advisor) ; Horký, Vojtěch (referee)
Dynamic adaptivity of a computer system is its ability to modify the behavior according to the environment in which it is executed. It allows the system to achieve better performance, but usually requires specialized architecture and brings more complexity. The thesis presents an analysis and design of a framework that allows simple and fluent performance-based adaptive development at the level of functions and methods. It closely examines the API requirements and possibilities of integrating such a framework into the Scala programming language using its advanced syntactical constructs. On theoretical level, it deals with the problem of selecting the most appropriate function to execute with given input based on measurements of previous executions. In the provided framework implementation, the main stress is laid on modularity and extensibility, as many possible future extensions are outlined. The solution is evaluated on a variety of development scenarios, ranging from input adaptation of algorithms to environment adaptations of complex distributed computations in Apache Spark.
Reification of the DEECo component model and its application in virtual-world simulations
Forch, Jan ; Bureš, Tomáš (advisor) ; Gemrot, Jakub (referee)
In the domain of dynamically evolving distributed systems composed of autonomous and (self-) adaptive components, the task of systematically managing the design complexity of their communication and composition is a pressing issue. This stems from the dynamic nature of such systems, where components and their bindings may appear and disappear without anticipation. As one way of addressing the challenge, [15] proposes a new component model (called DEECo), which features separation of concerns via a mechanism of dynamic implicit bindings with implicit communication. This leads to introduction of autonomous components and their dynamic interaction groups (called ensembles). The goal of the thesis is to reify the DEECo concepts and paradigms in a Java-based implementation connected to a simulation of a virtual world. As such, the thesis should provide a platform for experimentation with DEECo-based applications.
Adaptive Filters for Processing of Biological Signals
Strouhal, Martin ; Kozumplík, Jiří (referee) ; Provazník, Ivo (advisor)
This thesis is engaged in adaptive filtering. There are described common structure of adaptive systems and possible aplications. Most usual adaptive algoritms LMS and RLS are analysed. This project aims to realization of simple adaptive system and narrow bandstop. I researched qualities of the filters and the optimal setting. At the end both systems were tested using noisy ECG signal with varriable frequency of noise
Recursive Estimation of High-Order Markov Chains: Approximation by Finite Mixtures
Kárný, Miroslav
A high-order Markov chain is a universal model of stochastic relations between discrete-valued variables. The exact estimation of its transition probabilities suers from the curse of dimensionality. It requires an excessive amount of informative observations as well as an extreme memory for storing the corresponding su cient statistic. The paper bypasses this problem by considering a rich subset of Markov-chain models, namely, mixtures of low dimensional Markov chains, possibly with external variables. It uses Bayesian approximate estimation suitable for a subsequent decision making under uncertainty. The proposed recursive (sequential, one-pass) estimator updates a product of Dirichlet probability densities (pds) used as an approximate posterior pd, projects the result back to this class of pds and applies an improved data-dependent stabilised forgetting, which counteracts the dangerous accumulation of approximation errors.
Approximate Bayesian Recursive Estimation: On Approximation Errors
Kárný, Miroslav ; Dedecius, Kamil
Adaptive systems rely on recursive estimation of a firmly bounded complex- ity. As a rule, they have to use an approximation of the posterior proba- bility density function (pdf), which comprises unreduced information about the estimated parameter. In recursive setting, the latest approximate pdf is updated using the learnt system model and the newest data and then ap- proximated. The fact that approximation errors may accumulate over time course is mostly neglected in the estimator design and, at most, checked ex post. The paper inspects this problem.

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