In this thesis, we investigate the CDMA (Code Division Multiple Access) multi- user detection scheme where the DD (Decorrelating Detector) is used to eliminate the multiple access interference. The DD algorithm need to compute the inverse of the matrix involves a great deal of computation, especially when the number of users is large. A recursive method with the LMS (Least-Mean-Square) algorithm, namely the decorrelating transversal filter, to detect users¡¦ signals adaptively can reduce greatly the computational complexity of a CDMA multi-user detector. In this thesis, we focus on the hardware FPGA (Fdield Programmable Gate Array) implementation of the decorrelating transversal filter. The functional system simulation of the decorrelating transversal filter is carried out by using Matlab first. Then this filter is implemented by the Xilinx FPGA and its system performance is also verified.

Approved for public release; distribution is unlimited / Computer simulation studies of a frequency domain adaptive beamforming algorithm are presented. These simulation studies were conducted to determine the multiple broadband target localization capability and the full angular coverage capability of the algorithm. The algorithm was evaluated at several signal-to-noise ratios with varying sampling rates. The number of iterations that the adaptive algorithm took to reach a minimum estimation error was determined. Results of the simulation studies indicate that the algorithm can localize multiple broadband targets and has full angular coverage capability. / http://archive.org/details/computersimulati00behr / Lieutenant, United States Navy

Parameter identification of an induction machine is of great importance in numerous industrial applications, including the assessment of machine performance and design of control schemes. Parameter identification is based on the input-output signals and the model used. Many researches have applied the inverter drive to control the exciting signal of the induction machine in the identifying process. This study proposed a method to identify all parameter of the induction machine with a no-load low-voltage starting test. The method has a simple structure without needing extra hardware, which could significantly simplify the procedures and save cost. Based on the curves of resistance and reactance, the user can obtain the machine¡¦s equivalent circuit parameters. With the identified parameters of the equivalent circuit, input voltage, and rotor speed, the user can find the torque. From the torque and rotor speed, the user can find the mechanical parameters. A least mean square (LMS) method was used with a particle swarm optimization (PSO) method to solve the aforementioned problem. From various tests, the practicability and accuracy of this method can been proven. This study also proposes a method to rapidly analyze power parameters. This method uses two adjacent data to compute the fundamental frequency component of voltage or current. The parameters of fundamental frequency component include frequency, amplitude, and phase. Under the condition of varied parameters, the frequency and phase are dependent. This method fixes the frequency and computes the amplitude and phase, and then stable results will be obtained.

No description available.

No description available.

Self-excited systems are open loop unstable plants having a nonlinearity that prevents an exponentially increasing time response. The resulting limit cycle is induced by any slight disturbance that causes the response of the system to grow to the saturation level of the nonlinearity. Because there is no external disturbance, control of these self-excited systems requires that the open loop system dynamics are altered so that any unstable open loop poles are stabilized in the closed loop. This work examines a variety of adaptive control approaches for controlling a thermoacoustic instability, a physical self-excited system. Initially, a static feedback controller loopshaping design and associated system identification method is presented. This design approach is shown to effectively stabilize an unstable Rijke tube combustor while preventing the creation of additional controller induced instabilities. The loopshaping design method is then used in conjunction with a trained artificial neural network to demonstrate stabilizing control in the presence of changing plant dynamics over a wide variety of operating conditions. However, because the ANN is designed specifically for a single combustor/actuator arrangement, its limited portability is a distinct disadvantage. Filtered-X least mean squares (LMS) adaptive feedback control approaches are examined when applied to both stable and unstable plants. An identification method for approximating the relevant plant dynamics to be modeled is proposed and shown to effectively stabilize the self-excited system in simulations and experiments. The adaptive feedback controller is further analyzed for robust performance when applied to the stable, disturbance rejection control problem. It is shown that robust stability cannot be guaranteed because arbitrarily small errors in the plant model can generate gradient divergence and unstable feedback loops. Finally, a time-averaged-gradient (TAG) algorithm is investigated for use in controlling self-excited systems such as the thermoacoustic instability. The TAG algorithm is shown to be very effective in stabilizing the unstable dynamics using a variety of controller parameterizations, without the need for plant estimation information from the system to be controlled. / Ph. D.

L’objectif principal de cette thèse est de décliner et d’analyser l’algorithme kernel-LMS à noyau Gaussien dans trois cadres différents: celui des noyaux uniques et multiples, à valeurs réelles et à valeurs complexes, dans un contexte d’apprentissage distributé et coopératif dans les réseaux de capteurs. Plus précisement, ce travail s’intéresse à l’analyse du comportement en moyenne et en erreur quadratique de cas différents types d’algorithmes LMS à noyau. Les modèles analytiques de convergence obtenus sont validés par des simulations numérique. Tout d’abord, nous introduisons l’algorithme LMS, les espaces de Hilbert à noyau reproduisants, ainsi que les algorithmes de filtrage adaptatif à noyau existants. Puis, nous étudions analytiquement le comportement de l’algorithme LMS à noyau Gaussien dans le cas où les statistiques des éléments du dictionnaire ne répondent que partiellement aux statistiques des données d’entrée. Nous introduisons ensuite un algorithme LMS modifié à noyau basé sur une approche proximale. La stabilité de l’algorithme est également discutée. Ensuite, nous introduisons deux types d’algorithmes LMS à noyaux multiples. Nous nous concentrons en particulier sur l’analyse de convergence de l’un d’eux. Plus généralement, les caractéristiques des deux algorithmes LMS à noyaux multiples sont analysées théoriquement et confirmées par les simulations. L’algorithme LMS à noyau complexe augmenté est présenté et ses performances analysées. Enfin, nous proposons des stratégies de diffusion fonctionnelles dans les espaces de Hilbert à noyau reproduisant. La stabilité́ de cas de l’algorithme est étudiée. / The main objective of this thesis is to derive and analyze the Gaussian kernel least-mean-square (LMS) algorithm within three frameworks involving single and multiple kernels, real-valued and complex-valued, non-cooperative and cooperative distributed learning over networks. This work focuses on the stochastic behavior analysis of these kernel LMS algorithms in the mean and mean-square error sense. All the analyses are validated by numerical simulations. First, we review the basic LMS algorithm, reproducing kernel Hilbert space (RKHS), framework and state-of-the-art kernel adaptive filtering algorithms. Then, we study the convergence behavior of the Gaussian kernel LMS in the case where the statistics of the elements of the so-called dictionary only partially match the statistics of the input data. We introduced a modified kernel LMS algorithm based on forward-backward splitting to deal with $\ell_1$-norm regularization. The stability of the proposed algorithm is then discussed. After a review of two families of multikernel LMS algorithms, we focus on the convergence behavior of the multiple-input multikernel LMS algorithm. More generally, the characteristics of multikernel LMS algorithms are analyzed theoretically and confirmed by simulation results. Next, the augmented complex kernel LMS algorithm is introduced based on the framework of complex multikernel adaptive filtering. Then, we analyze the convergence behavior of algorithm in the mean-square error sense. Finally, in order to cope with the distributed estimation problems over networks, we derive functional diffusion strategies in RKHS. The stability of the algorithm in the mean sense is analyzed.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Adaptive filtering is applied as solution for many problems in engineer. There are many techniques to improve adaptive filtering as kernel methods and, in addiction, it is used a pretuned dictionary. In this context, here is presented the KSIG algorithm, the kernel version of Sigmoide, where is used the kernel, to decrease the error, and the non-linear and even cost function to increase the convergence speed. Here it is described also, the KSIG with a pretuned dictionary, to reduce the size of the data set used to calculate the filter output, which is a kernel method consequence . The KSIG and KSIG with pre-tuned dictionary theoretical efficiency is one result of their convergence proof, which evidence that the algorithms converge in average. The learning curves, which are results of some experiments, show that when KSIG and KLMS algorithms are compared, the first converges faster, in less iterations, than the second, in the version with and without pre-tuned dictionary of both algorithms. / A filtragem adaptativa é aplicada na solução de diversos problemas da engenharia. Há muitas alternativas para melhorá-la, uma delas é o uso de kernel e, em adição, o uso de um dicionário pré-definido de dados. Neste contexto, este trabalho apresenta o KSIG, a versão em kernel do algoritmo Sigmoide, um algoritmo que otimiza o erro do filtro pelo emprego de uma função de custo par e não linear. Ademais, é apresentada a versão do KSIG com dicionário de dados pré-definido, visando redução do grande número de dados utilizados para obtenção da saída decorrente do uso da técnica com kernel. A eficiência teórica do KSIG e de sua versão com dicionário pré-definido é um resultado presente nas provas de convergência construídas para ambos os algoritmos, as quais demonstraram que estes convergem em média. Já as curvas de aprendizagem obtidas nas simulações computacionais dos experimentos realizados demonstraram que o KSIG quando comparado ao KLMS, em diferentes problemas de filtragem adaptativa, apresenta convergência mais rápida, em menos iterações, tanto nas versões sem tanto com dicionário pré-definido de ambos os algoritmos.

Este trabalho apresenta um método para a estimação da freqüência em sistemas elétricos de potência utilizando filtros adaptativos baseados no algoritmo dos mínimos quadrados (LMS - least mean square). A análise do sistema de potência é realizada através da conversão das tensões trifásicas em um sinal complexo pela aplicação da transformada \'alfa\'\'beta\', cuja forma complexa foi direcionada ao algoritmo de filtragem adaptativa. O método é baseado na aplicação da filtragem adaptativa para a realização de rastreio do sinal de entrada, o que permite verificar o seu comportamento variante no tempo. O algoritmo proposto foi testado através de formas de ondas geradas com o software Matlab e de simulações realizadas através do software Alternative Transients Program (ATP). É importante salientar que nas simulações do ATP foram modelados diversos equipamentos que constituem o sistema elétrico de potência, incluindo um gerador síncrono com regulação de velocidade, linhas de transmissão com variação em freqüência e transformadores de potência com suas respectivas curvas de saturação. Estas modelagens tiveram por objetivo gerar dados das mais diversas e distintas situações para a verificação e análise da metodologia proposta. Os resultados da pesquisa mostram a excelência na aplicabilidade do algoritmo proposto na estimação da freqüência de um sistema elétrico, mesmo com sinais ruidosos, além do rastreio fiel da freqüência em situações de manobra e operação. Alguns dos resultados apresentados comparam as estimações obtidas pela técnica proposta em relação às estimações de um determinado relé comercial, habilitado à supervisão da freqüência. / This work presents a method for frequency estimation in power systems using adaptive filters based in the algorithm of least mean square (LMS). The analysis of the power system is made through the conversion of the three-phase voltages in a complex signal with the application of \'alfa\'\'beta\' transform, whose complex form was directed to the algorithm of adaptive filtering. The method is based on the application of the adaptive filtering for tracking the input signal, and it allows verifying its variant behavior in time. The algorithm was tested through waveforms generated by Matlab software and simulations carried out through Alternative Transients Program (ATP) software. It is important to point out that in the simulations using ATP many diferent power system equipments had been modeled, including a synchronous generator with speed regulation, transmission lines with variation in frequency and power transformers with their saturation curves. The objective of these tests was to generate data for diverse and distinct situations for the verification and the analysis of the proposed methodology. The results of the research show the excellence in the applicability of the algorithm considered in frequency estimation of an electrical system, even with noisy signals, as well as the tracking of the frequency during operation. Some of the results are compared to the ones presented by a commercial relay set to track frequency.

Este trabalho apresenta um método para a estimação da freqüência em sistemas elétricos de potência utilizando filtros adaptativos baseados no algoritmo dos mínimos quadrados (LMS - least mean square). A análise do sistema de potência é realizada através da conversão das tensões trifásicas em um sinal complexo pela aplicação da transformada \'alfa\'\'beta\', cuja forma complexa foi direcionada ao algoritmo de filtragem adaptativa. O método é baseado na aplicação da filtragem adaptativa para a realização de rastreio do sinal de entrada, o que permite verificar o seu comportamento variante no tempo. O algoritmo proposto foi testado através de formas de ondas geradas com o software Matlab e de simulações realizadas através do software Alternative Transients Program (ATP). É importante salientar que nas simulações do ATP foram modelados diversos equipamentos que constituem o sistema elétrico de potência, incluindo um gerador síncrono com regulação de velocidade, linhas de transmissão com variação em freqüência e transformadores de potência com suas respectivas curvas de saturação. Estas modelagens tiveram por objetivo gerar dados das mais diversas e distintas situações para a verificação e análise da metodologia proposta. Os resultados da pesquisa mostram a excelência na aplicabilidade do algoritmo proposto na estimação da freqüência de um sistema elétrico, mesmo com sinais ruidosos, além do rastreio fiel da freqüência em situações de manobra e operação. Alguns dos resultados apresentados comparam as estimações obtidas pela técnica proposta em relação às estimações de um determinado relé comercial, habilitado à supervisão da freqüência. / This work presents a method for frequency estimation in power systems using adaptive filters based in the algorithm of least mean square (LMS). The analysis of the power system is made through the conversion of the three-phase voltages in a complex signal with the application of \'alfa\'\'beta\' transform, whose complex form was directed to the algorithm of adaptive filtering. The method is based on the application of the adaptive filtering for tracking the input signal, and it allows verifying its variant behavior in time. The algorithm was tested through waveforms generated by Matlab software and simulations carried out through Alternative Transients Program (ATP) software. It is important to point out that in the simulations using ATP many diferent power system equipments had been modeled, including a synchronous generator with speed regulation, transmission lines with variation in frequency and power transformers with their saturation curves. The objective of these tests was to generate data for diverse and distinct situations for the verification and the analysis of the proposed methodology. The results of the research show the excellence in the applicability of the algorithm considered in frequency estimation of an electrical system, even with noisy signals, as well as the tracking of the frequency during operation. Some of the results are compared to the ones presented by a commercial relay set to track frequency.