Based on the Lyapunov stability theorem, a decentralized adaptive sliding mode control scheme is proposed in this thesis for a class of mismatched perturbed large-scale systems containing dead-zone input to solve regulation problems. The main idea is that some adaptive mechanisms are embedded both in the sliding surface and in the controllers, so that not only the mismatched perturbations are suppressed during the sliding mode, but also the information of upper bound of perturbations is not required. The sliding surface function is firstly designed through the usage of a pseudo controller which is capable of stabilizing the reduced-order systems. The second step is to design the controllers so that the trajectories of the controlled systems are able to reach sliding surface in a finite time. Once the controlled system enters the sliding mode, the asymptotical stability is guaranteed for each subsystem even the mismatched perturbations exist. A numerical example and a practical example are given to demonstrate the feasibility of the proposed design technique.

Based on the Lyapunov stability theorem, an adaptive sliding mode control scheme is proposed in this thesis for a class of systems with mismatched state-delayed perturbations to solve regulation problems. The main idea is that some adaptive mechanisms are embedded both in the sliding surfaces and in the controllers, so that not only the mismatched perturbations are suppressed during the sliding mode, but also the information of upper bound of perturbations is not required. The sliding surface functions are firstly designed through the usage of designed pseudo controllers, which is capable of stabilizing the reduced-order systems. The number of the sliding surface functions required by the proposed control scheme depends on the relationship between systems's dimension and number of inputs. The second step is to design the controllers so that the trajectories of the controlled system are able to reach sliding surface in a finite time. Once the controlled system enters the sliding mode, the asymptotical stability is guaranteed. Two numerical examples and one practical experiment are given for demonstrating the feasibility of the proposed control scheme.

Based on the Lyapunov stability theorem, an adaptive sliding mode control scheme is proposed in this thesis for a class of mismatched perturbed multi-input multi-output (MIMO) dynamic systems to solve regualtion problems. The sliding surface function is firstly designed by treating some state variables as a pseudo controllers through the usage of sliding function to stabilize the rest of state variables. In this thesis the number of these pseudo controllers is less than that of the state variables to be stabilized. The second step is to design the controllers so that the trajectories of the controlled systems are able to reach sliding surface in a finite time. Some adaptive mechanisms are embedded in the sliding surface function and sliding mode controllers, so that not only the mismatched perturbations can be suppressed during the sliding mode, but also the information of upper bounds of some perturbations are not required when designing the sliding surface function and controllers. Once the controlled system enters the sliding mode, the state trajectories can achieve asymptotical stability under certain conditions. A numerical example and a practical example are given to demonstrate the feasibility of the proposed design technique.

Based on the Lyapunov stability theorem, an adaptive sliding mode control scheme is proposed in this thesis for a class of mismatched perturbed multi-input multi-output (MIMO) dynamic systems to solve stabilization problems. In order to suppress the perturbations in the control systems, adaptive mechanisms are employed both in sliding function and control effort, so that the information of upperbound of some perturbations is not required when designing the proposed control scheme. Due to the novel design of sliding function, the state trajectories of this system can achieve asymptotical stability in the sliding mode even if mismatched perturbations exist. In addition, with an adaptive mechanism embedded in the proposed control scheme, the controller can drive the state's trajectory into the designated sliding surface in a finite time. A numerical example is demonstrated for showing the applicability of the proposed design technique.

Based on the Lyapunov stability theorem, an adaptive variable structure observer and a controller are proposed in this thesis for a class of mismatched perturbed multi-input multi-output (MIMO) dynamic systems with unmeasurable states to solve regulation and tracking problems. In order to estimate the unmeasurable states, a design methodology of variable structure observers is presented first. Then the controller is designed so that the trajectories of the controlled systems are able to reach sliding surface in a finite time. Some adaptive mechanisms are embedded in the sliding surface function and sliding mode controllers, so that not only the mismatched perturbations are suppressed effectively during the sliding mode, but also the information of upper bounds of some perturbations are not required. When the controlled system is the sliding mode, the stability or asymptotical stability is guaranteed. A numerical example and a practical example are given to demonstrate the feasibility of the proposed design technique.

Šiame darbe sprendžiamas multiplikatyviųjų funkcijų reikšmių sumavimo uždavinys. Nagrinėjama klasė , kuriai priklauso funkcijos, tenkinančios keletą sąlygų. Svarbiausia iš jų: . Čia C – konstanta, o M – pakankamai didelis, bet fiksuotas teigiamas realusis skaičius. Šios sąlygos prasmė: klasės funkcijos pirminių skaičių aibėje yra artimos vienetui. Darbe įrodyta teorema: jei , tai kai , teisinga asimptotinė formulė . Čia tam tikra konstanta, priklausanti nuo funkcijos . / The problem of an asymptotical behaviour of values of multiplicative functions is solved in this work. The class is defined with some conditions. The most important condition is: , C is a constant, M is a sufficiently large real positive number here. The following theorem is proved: if function belongs to the class then when . A constant depends on function .

Die vorliegende Diplomarbeit beschäftigt sich mit zwei Aspekten der statistischen Eigenschaften von Clusterverfahren. Zum einen geht die Arbeit auf die Frage der Existenz von unterschiedlichen Clusteranalysemethoden zur Strukturfindung und deren unterschiedlichen Vorgehensweisen ein. Die Methode des Abstandes zwischen Mannigfaltigkeiten und die K-means Methode liefern ausgehend von gleichen Daten unterschiedliche Endclusterungen. Der zweite Teil dieser Arbeit beschäftigt sich näher mit den asymptotischen Eigenschaften des K-means Verfahrens. Hierbei ist die Menge der optimalen Clusterzentren konsistent. Bei Vergrößerung des Stichprobenumfangs gegen Unendlich konvergiert diese in Wahrscheinlichkeit gegen die Menge der Clusterzentren, die das Varianzkriterium minimiert. Ebenfalls konvergiert die Menge der optimalen Clusterzentren für n gegen Unendlich gegen eine Normalverteilung. Es hat sich dabei ergeben, dass die einzelnen Clusterzentren voneinander abhängen. / The following thesis describes two different views onto the statistical characterics of clustering procedures. At first it adresses the questions whether different clustering methods exist to ascertain the structure of clusters and in what ays the strategies of these methods differ from each other. The method of distance between the manifolds as well as the k-means method provide different final clusters based on equal initial data. The second part of the thesis concentrates on asymptotic properties of the k-means procedure. Here the amount of optimal clustering centres is consistent. If the size of the sample range is enlarged towards infinity, it also converges in probability towards the amount of clustering centres which minimized the whithin cluster sum of squares. Likewise the amount of optimal clustering centres converges for infinity towards the normal distribution. The main result shows that the individual clustering centres are dependent on each other.

Wir betrachten ein System nichtlinearer parabolischer partieller Differentialgleichungen zur Modellierung des biologischen Phänomens Chemotaxis, das unter anderem in Aggregationsprozessen in Lebenszyklen bestimmter Einzeller eine wichtige Rolle spielt. Unser Chemotaxismodell benutzt Sensitivitäts funktionen, die die vorkommenden biologischen Prozesse genauer spezifizieren. Trotz der durch die Sensitivitätsfunktionen eingebrachten, zusätzlichen Nichtlinearitäten in den Gleichungen erhalten wir zeitlich globale Existenz von Lösungen für verschiedene biologisch realistische Klassen von Sensitivitätsfunktionen und können unter unterschiedlichen Bedingungen an die Systemdaten Konvergenz der Lösungen zu trivialen und nicht-trivialen stationären Punkten beweisen. / We consider a system of non-linear parabolic partial differential equations modeling chemotaxis, a biological phenomenon which plays a crucial role in aggregation processes in the life cycle of certain unicellular organisms. Our chemotaxis model introduces sensitivity functions which help describe the biological processes more accurately. In spite of the additional non-linearities introduced by the sensitivity functions into the equations, we obtain global existence of solutions for different classes of biologically realistic sensitivity functions and can prove convergence of the solutions to trivial and non-trivial steady states.

<p>In this thesis we study a scalar uniform and non-uniform quantization of random processes (or signals) in average case setting. Quantization (or discretization) of a signal is a standard task in all nalog/digital devices (e.g., digital recorders, remote sensors etc.). We evaluate the necessary memory capacity (or quantization rate) needed for quantized process realizations by exploiting the correlation structure of the model random process. The thesis consists of an introductory survey of the subject and related theory followed by four included papers (A-D).</p><p>In Paper A we develop a quantization coding method when quantization levels crossings by a process realization are used for its coding. Asymptotical behavior of mean quantization rate is investigated in terms of the correlation structure of the original process. For uniform and non-uniform quantization, we assume that the quantization cellwidth tends to zero and the number of quantization levels tends to infinity, respectively.</p><p>In Papers B and C we focus on an additive noise model for a quantized random process. Stochastic structures of asymptotic quantization errors are derived for some bounded and unbounded non-uniform quantizers when the number of quantization levels tends to infinity. The obtained results can be applied, for instance, to some optimization design problems for quantization levels.</p><p>Random signals are quantized at sampling points with further compression. In Paper D the concern is statistical inference for run-length encoding (RLE) method, one of the compression techniques, applied to quantized stationary Gaussian sequences. This compression method is widely used, for instance, in digital signal and image processing. First, we deal with mean RLE quantization rates for various probabilistic models. For a time series with unknown stochastic structure, we investigate asymptotic properties (e.g., asymptotic normality) of two estimates for the mean RLE quantization rate based on an observed sample when the sample size tends to infinity.</p><p>These results can be used in communication theory, signal processing, coding, and compression applications. Some examples and numerical experiments demonstrating applications of the obtained results for synthetic and real data are presented.</p>

In this thesis we study a scalar uniform and non-uniform quantization of random processes (or signals) in average case setting. Quantization (or discretization) of a signal is a standard task in all nalog/digital devices (e.g., digital recorders, remote sensors etc.). We evaluate the necessary memory capacity (or quantization rate) needed for quantized process realizations by exploiting the correlation structure of the model random process. The thesis consists of an introductory survey of the subject and related theory followed by four included papers (A-D). In Paper A we develop a quantization coding method when quantization levels crossings by a process realization are used for its coding. Asymptotical behavior of mean quantization rate is investigated in terms of the correlation structure of the original process. For uniform and non-uniform quantization, we assume that the quantization cellwidth tends to zero and the number of quantization levels tends to infinity, respectively. In Papers B and C we focus on an additive noise model for a quantized random process. Stochastic structures of asymptotic quantization errors are derived for some bounded and unbounded non-uniform quantizers when the number of quantization levels tends to infinity. The obtained results can be applied, for instance, to some optimization design problems for quantization levels. Random signals are quantized at sampling points with further compression. In Paper D the concern is statistical inference for run-length encoding (RLE) method, one of the compression techniques, applied to quantized stationary Gaussian sequences. This compression method is widely used, for instance, in digital signal and image processing. First, we deal with mean RLE quantization rates for various probabilistic models. For a time series with unknown stochastic structure, we investigate asymptotic properties (e.g., asymptotic normality) of two estimates for the mean RLE quantization rate based on an observed sample when the sample size tends to infinity. These results can be used in communication theory, signal processing, coding, and compression applications. Some examples and numerical experiments demonstrating applications of the obtained results for synthetic and real data are presented.