Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

HF spectrum activity prediction model based on HMM for cognitive radio applications

Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band. By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved. These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.

Loss model for a high frequency and low load DC/DC synchronous buck converter

This work presents a behavioral-analytical hybrid loss model for a buck converter. The model has been designed for a wide operating frequency range up to 4MHz and a low power range (below 20W). It is focused on the switching losses obtained in the power MOSFETs. Main advantages of the model are the fast calculation time and a good accuracy. It has been validated by simulation and experimentally with one Ga, power transistor and two Si MOSFETs. Results show good agreement between measurements and the model.

Hybrid behavioral-analytical loss model for a high frequency and low load DC/DC buck converter

This work presents a behavioral-analytical hybrid loss model for a buck converter. The model has been designed for a wide operating frequency range up to 4MHz and a low power range (below 20W). It is focused on the switching losses obtained in the power MOSFETs. Main advantages of the model are the fast calculation time (below 8.5 seconds) and a good accuracy, which makes this model suitable for the optimization process of the losses in the design of a converter. It has been validated by simulation and experimentally with one GaN power transistor and three Si MOSFETs. Results show good agreement between measurements and the model

Variable structure control with chattering reduction of a generalized T-S model

In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. New functions for chattering reduction and error convergence without sacrificing invariant properties are proposed. The main feature of the proposed method is that the switching function is added as an additional fuzzy variable and will be introduced in the premise part of the fuzzy rules; together with the state variables. In this work, a tuning of the well known weighting parameters approach is proposed to optimize local and global approximation and modelling capability of the Takagi-Sugeno (T-S) fuzzy model to improve the choice of the performance index and minimize it. The main problem encountered is that the T-S identification method can not be applied when the membership functions are overlapped by pairs. This in turn restricts the application of the T-S method because this type of membership function has been widely used in control applications. The approach developed here can be considered as a generalized version of the T-S method. An inverted pendulum mounted on a cart is chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of the proposed estimation approach in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the chattering reduction algorithm. In this paper, we prove that the proposed estimation algorithm converge the very fast, thereby making it very practical to use. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved.

Cooperative off-policy prediction of markov decision processes in adaptive networks

We apply diffusion strategies to propose a cooperative reinforcement learning algorithm, in which agents in a network communicate with their neighbors to improve predictions about their environment. The algorithm is suitable to learn off-policy even in large state spaces. We provide a mean-square-error performance analysis under constant step-sizes. The gain of cooperation in the form of more stability and less bias and variance in the prediction error, is illustrated in the context of a classical model. We show that the improvement in performance is especially significant when the behavior policy of the agents is different from the target policy under evaluation.

Analysis of gain-switching in two-section tapered lasers

We analyze the gain-switching dynamics of two-section tapered lasers by means of a simplified three-rate-equation model. The goal is to improve the understanding of the underlying physics and to optimize the device geometry to achieve high power short duration optical pulses.

On-road visual vehicle tracking using Markov chain Monte Carlo with metropolis sampling

In this study, a method for vehicle tracking through video analysis based on Markov chain Monte Carlo (MCMC) particle filtering with metropolis sampling is proposed. The method handles multiple targets with low computational requirements and is, therefore, ideally suited for advanced-driver assistance systems that involve real-time operation. The method exploits the removed perspective domain given by inverse perspective mapping (IPM) to define a fast and efficient likelihood model. Additionally, the method encompasses an interaction model using Markov Random Fields (MRF) that allows treatment of dependencies between the motions of targets. The proposed method is tested in highway sequences and compared to state-of-the-art methods for vehicle tracking, i.e., independent target tracking with Kalman filtering (KF) and joint tracking with particle filtering. The results showed fewer tracking failures using the proposed method.

Wide bandwidth envelope trackers with reduced switching frequency for RF power amplifier

El requerimiento de proveer alta frecuencia de datos en los modernos sistema de comunicación inalámbricos resulta en complejas señales moduladas de radio-frequencia (RF) con un gran ancho de banda y alto ratio pico-promedio (PAPR). Para garantizar la linealidad del comportamiento, los amplificadores lineales de potencia comunes funcionan típicamente entre 4 y 10 dB de back-o_ desde la máxima potencia de salida, ocasionando una baja eficiencia del sistema. La eliminación y restauración de la evolvente (EER) y el seguimiento de la evolvente (ET) son dos prometedoras técnicas para resolver el problema de la eficiencia. Tanto en EER como en ET, es complicado diseñar un amplificador de potencia que sea eficiente para señales de RF de alto ancho de banda y alto PAPR. Una propuesta común para los amplificadores de potencia es incluir un convertidor de potencia de muy alta eficiencia operando a frecuencias más altas que el ancho de banda de la señal RF. En este caso, la potencia perdida del convertidor ocasionado por la alta frecuencia desaconseja su práctica cuando el ancho de banda es muy alto. La solución a este problema es el enfoque de esta disertación que presenta dos arquitecturas de amplificador evolvente: convertidor híbrido-serie con una técnica de evolvente lenta y un convertidor multinivel basado en un convertidor reductor multifase con control de tiempo mínimo. En la primera arquitectura, una topología híbrida está compuesta de una convertidor reductor conmutado y un regulador lineal en serie que trabajan juntos para ajustar la tensión de salida para seguir a la evolvente con precisión. Un algoritmo de generación de una evolvente lenta crea una forma de onda con una pendiente limitada que es menor que la pendiente máxima de la evolvente original. La salida del convertidor reductor sigue esa forma de onda en vez de la evolvente original usando una menor frecuencia de conmutación, porque la forma de onda no sólo tiene una pendiente reducida sino también un menor ancho de banda. De esta forma, el regulador lineal se usa para filtrar la forma de onda tiene una pérdida de potencia adicional. Dependiendo de cuánto se puede reducir la pendiente de la evolvente para producir la forma de onda, existe un trade-off entre la pérdida de potencia del convertidor reductor relacionada con la frecuencia de conmutación y el regulador lineal. El punto óptimo referido a la menor pérdida de potencia total del amplificador de evolvente es capaz de identificarse con la ayuda de modelo preciso de pérdidas que es una combinación de modelos comportamentales y analíticos de pérdidas. Además, se analiza el efecto en la respuesta del filtro de salida del convertidor reductor. Un filtro de dampeo paralelo extra es necesario para eliminar la oscilación resonante del filtro de salida porque el convertidor reductor opera en lazo abierto. La segunda arquitectura es un amplificador de evolvente de seguimiento de tensión multinivel. Al contrario que los convertidores que usan multi-fuentes, un convertidor reductor multifase se emplea para generar la tensión multinivel. En régimen permanente, el convertidor reductor opera en puntos del ciclo de trabajo con cancelación completa del rizado. El número de niveles de tensión es igual al número de fases de acuerdo a las características del entrelazamiento del convertidor reductor. En la transición, un control de tiempo mínimo (MTC) para convertidores multifase es novedosamente propuesto y desarrollado para cambiar la tensión de salida del convertidor reductor entre diferentes niveles. A diferencia de controles convencionales de tiempo mínimo para convertidores multifase con inductancia equivalente, el propuesto MTC considera el rizado de corriente por cada fase basado en un desfase fijo que resulta en diferentes esquemas de control entre las fases. La ventaja de este control es que todas las corrientes vuelven a su fase en régimen permanente después de la transición para que la siguiente transición pueda empezar muy pronto, lo que es muy favorable para la aplicación de seguimiento de tensión multinivel. Además, el control es independiente de la carga y no es afectado por corrientes de fase desbalanceadas. Al igual que en la primera arquitectura, hay una etapa lineal con la misma función, conectada en serie con el convertidor reductor multifase. Dado que tanto el régimen permanente como el estado de transición del convertidor no están fuertemente relacionados con la frecuencia de conmutación, la frecuencia de conmutación puede ser reducida para el alto ancho de banda de la evolvente, la cual es la principal consideración de esta arquitectura. La optimización de la segunda arquitectura para más alto anchos de banda de la evolvente es presentada incluyendo el diseño del filtro de salida, la frecuencia de conmutación y el número de fases. El área de diseño del filtro está restringido por la transición rápida y el mínimo pulso del hardware. La rápida transición necesita un filtro pequeño pero la limitación del pulso mínimo del hardware lleva el diseño en el sentido contrario. La frecuencia de conmutación del convertidor afecta principalmente a la limitación del mínimo pulso y a las pérdidas de potencia. Con una menor frecuencia de conmutación, el ancho de pulso en la transición es más pequeño. El número de fases relativo a la aplicación específica puede ser optimizado en términos de la eficiencia global. Otro aspecto de la optimización es mejorar la estrategia de control. La transición permite seguir algunas partes de la evolvente que son más rápidas de lo que el hardware puede soportar al precio de complejidad. El nuevo método de sincronización de la transición incrementa la frecuencia de la transición, permitiendo que la tensión multinivel esté más cerca de la evolvente. Ambas estrategias permiten que el convertidor pueda seguir una evolvente con un ancho de banda más alto que la limitación de la etapa de potencia. El modelo de pérdidas del amplificador de evolvente se ha detallado y validado mediante medidas. El mecanismo de pérdidas de potencia del convertidor reductor tiene que incluir las transiciones en tiempo real, lo cual es diferente del clásico modelos de pérdidas de un convertidor reductor síncrono. Este modelo estima la eficiencia del sistema y juega un papel muy importante en el proceso de optimización. Finalmente, la segunda arquitectura del amplificador de evolvente se integra con el amplificador de clase F. La medida del sistema EER prueba el ahorro de energía con el amplificador de evolvente propuesto sin perjudicar la linealidad del sistema. ABSTRACT The requirement of delivering high data rates in modern wireless communication systems results in complex modulated RF signals with wide bandwidth and high peak-to-average ratio (PAPR). In order to guarantee the linearity performance, the conventional linear power amplifiers typically work at 4 to 10 dB back-off from the maximum output power, leading to low system efficiency. The envelope elimination and restoration (EER) and envelope tracking (ET) are two promising techniques to overcome the efficiency problem. In both EER and ET, it is challenging to design efficient envelope amplifier for wide bandwidth and high PAPR RF signals. An usual approach for envelope amplifier includes a high-efficiency switching power converter operating at a frequency higher than the RF signal's bandwidth. In this case, the power loss of converter caused by high switching operation becomes unbearable for system efficiency when signal bandwidth is very wide. The solution of this problem is the focus of this dissertation that presents two architectures of envelope amplifier: a hybrid series converter with slow-envelope technique and a multilevel converter based on a multiphase buck converter with the minimum time control. In the first architecture, a hybrid topology is composed of a switched buck converter and a linear regulator in series that work together to adjust the output voltage to track the envelope with accuracy. A slow envelope generation algorithm yields a waveform with limited slew rate that is lower than the maximum slew rate of the original envelope. The buck converter's output follows this waveform instead of the original envelope using lower switching frequency, because the waveform has not only reduced slew rate but also reduced bandwidth. In this way, the linear regulator used to filter the waveform has additional power loss. Depending on how much reduction of the slew rate of envelope in order to obtain that waveform, there is a trade-off between the power loss of buck converter related to the switching frequency and the power loss of linear regulator. The optimal point referring to the lowest total power loss of this envelope amplifier is identified with the help of a precise power loss model that is a combination of behavioral and analytic loss model. In addition, the output filter's effect on the response is analyzed. An extra parallel damping filter is needed to eliminate the resonant oscillation of output filter L and C, because the buck converter operates in open loop. The second architecture is a multilevel voltage tracking envelope amplifier. Unlike the converters using multi-sources, a multiphase buck converter is employed to generate the multilevel voltage. In the steady state, the buck converter operates at complete ripple cancellation points of duty cycle. The number of the voltage levels is equal to the number of phases according the characteristics of interleaved buck converter. In the transition, a minimum time control (MTC) for multiphase converter is originally proposed and developed for changing the output voltage of buck converter between different levels. As opposed to conventional minimum time control for multiphase converter with equivalent inductance, the proposed MTC considers the current ripple of each phase based on the fixed phase shift resulting in different control schemes among the phases. The advantage of this control is that all the phase current return to the steady state after the transition so that the next transition can be triggered very soon, which is very favorable for the application of multilevel voltage tracking. Besides, the control is independent on the load condition and not affected by the unbalance of phase current. Like the first architecture, there is also a linear stage with the same function, connected in series with the multiphase buck converter. Since both steady state and transition state of the converter are not strongly related to the switching frequency, it can be reduced for wide bandwidth envelope which is the main consideration of this architecture. The optimization of the second architecture for wider bandwidth envelope is presented including the output filter design, switching frequency and the number of phases. The filter design area is restrained by fast transition and the minimum pulse of hardware. The fast transition needs small filter but the minimum pulse of hardware limitation pushes the filter in opposite way. The converter switching frequency mainly affects the minimum pulse limitation and the power loss. With lower switching frequency, the pulse width in the transition is smaller. The number of phases related to specific application can be optimized in terms of overall efficiency. Another aspect of optimization is improving control strategy. Transition shift allows tracking some parts of envelope that are faster than the hardware can support at the price of complexity. The new transition synchronization method increases the frequency of transition, allowing the multilevel voltage to be closer to the envelope. Both control strategies push the converter to track wider bandwidth envelope than the limitation of power stage. The power loss model of envelope amplifier is detailed and validated by measurements. The power loss mechanism of buck converter has to include the transitions in real time operation, which is different from classical power loss model of synchronous buck converter. This model estimates the system efficiency and play a very important role in optimization process. Finally, the second envelope amplifier architecture is integrated with a Class F amplifier. EER system measurement proves the power saving with the proposed envelope amplifier without disrupting the linearity performance.