Equalização neural aplicada a sistemas com modulação bidimensional em fibra óptica

Nowadays, optic fiber is one of the most used communication methods, mainly due to the fact that the data transmission rates of those systems exceed all of the other means of digital communication. Despite the great advantage, there are problems that prevent full utilization of the optical channel: by increasing the transmission speed and the distances involved, the data is subjected to non-linear inter symbolic interference caused by the dispersion phenomena in the fiber. Adaptive equalizers can be used to solve this problem, they compensate non-ideal responses of the channel in order to restore the signal that was transmitted. This work proposes an equalizer based on artificial neural networks and evaluates its performance in optical communication systems. The proposal is validated through a simulated optic channel and the comparison with other adaptive equalization techniques

Análise dinâmica de contingências de sistemas de energia elétrica por redes neurais baseadas na teoria da ressonância adaptativa

Pós-graduação em Engenharia Elétrica - FEIS

Modelos de dispositivos de microondas e ópticos através de redes neurais artificiais de alimentação direta

This dissertation contributes for the development of methodologies through feed forward artificial neural networks for microwave and optical devices modeling. A bibliographical revision on the applications of neuro-computational techniques in the areas of microwave/optical engineering was carried through. Characteristics of networks MLP, RBF and SFNN, as well as the strategies of supervised learning had been presented. Adjustment expressions of the networks free parameters above cited had been deduced from the gradient method. Conventional method EM-ANN was applied in the modeling of microwave passive devices and optical amplifiers. For this, they had been proposals modular configurations based in networks SFNN and RBF/MLP objectifying a bigger capacity of models generalization. As for the training of the used networks, the Rprop algorithm was applied. All the algorithms used in the attainment of the models of this dissertation had been implemented in Matlab

Desenvolvimento de redes neurais para previsão de cargas elétricas de sistemas de energia elétrica

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Detecção e classificação de VTCDs em sistemas de distribuição de energia elétrica usando redes neurais artificiais.

O objetivo deste trabalho é conhecer e compreender melhor os imprevistos no fornecimento de energia elétrica, quando ocorrem as variações de tensão de curta duração (VTCD). O banco de dados necessário para os diagnósticos das faltas foi obtido através de simulações de um modelo de alimentador radial através do software PSCAD/EMTDC. Este trabalho utiliza um Phase-Locked Loop (PLL) com o intuito de detectar VTCDs e realizar a estimativa automática da frequência, do ângulo de fase e da amplitude das tensões e correntes da rede elétrica. Nesta pesquisa, desenvolveram-se duas redes neurais artificiais: uma para identificar e outra para localizar as VTCDs ocorridas no sistema de distribuição de energia elétrica. A técnica aqui proposta aplica-se a alimentadores trifásicos com cargas desequilibradas, que podem possuir ramais laterais trifásicos, bifásicos e monofásicos. No desenvolvimento da mesma, considera-se que há disponibilidade de medições de tensões e correntes no nó inicial do alimentador e também em alguns pontos esparsos ao longo do alimentador de distribuição. Os desempenhos das arquiteturas das redes neurais foram satisfatórios e demonstram a viabilidade das RNAs na obtenção das generalizações que habilitam o sistema para realizar a classificação de curtos-circuitos.

Contribuições à análise de robustez de sistemas de controle usando redes neurais

This work develops a robustness analysis with respect to the modeling errors, being applied to the strategies of indirect control using Artificial Neural Networks - ANN s, belong to the multilayer feedforward perceptron class with on-line training based on gradient method (backpropagation). The presented schemes are called Indirect Hybrid Control and Indirect Neural Control. They are presented two Robustness Theorems, being one for each proposed indirect control scheme, which allow the computation of the maximum steady-state control error that will occur due to the modeling error what is caused by the neural identifier, either for the closed loop configuration having a conventional controller - Indirect Hybrid Control, or for the closed loop configuration having a neural controller - Indirect Neural Control. Considering that the robustness analysis is restrict only to the steady-state plant behavior, this work also includes a stability analysis transcription that is suitable for multilayer perceptron class of ANN s trained with backpropagation algorithm, to assure the convergence and stability of the used neural systems. By other side, the boundness of the initial transient behavior is assured by the assumption that the plant is BIBO (Bounded Input, Bounded Output) stable. The Robustness Theorems were tested on the proposed indirect control strategies, while applied to regulation control of simulated examples using nonlinear plants, and its results are presented

Detecção e isolamento de falhas em sistemas dinâmicos baseados em redes neurais

This master dissertation presents the development of a fault detection and isolation system based in neural network. The system is composed of two parts: an identification subsystem and a classification subsystem. Both of the subsystems use neural network techniques with multilayer perceptron training algorithm. Two approaches for identifica-tion stage were analyzed. The fault classifier uses only residue signals from the identification subsystem. To validate the proposal we have done simulation and real experiments in a level system with two water reservoirs. Several faults were generated above this plant and the proposed fault detection system presented very acceptable behavior. In the end of this work we highlight the main difficulties found in real tests that do not exist when it works only with simulation environments

Identificação remota de sistemas operacionais utilizando análise de processos aleatórios e redes neurais artificiais

A new method to perform TCP/IP fingerprinting is proposed. TCP/IP fingerprinting is the process of identify a remote machine through a TCP/IP based computer network. This method has many applications related to network security. Both intrusion and defence procedures may use this process to achieve their objectives. There are many known methods that perform this process in favorable conditions. However, nowadays there are many adversities that reduce the identification performance. This work aims the creation of a new OS fingerprinting tool that bypass these actual problems. The proposed method is based on the use of attractors reconstruction and neural networks to characterize and classify pseudo-random numbers generators

Controle inteligente de sistemas eletroidráulicos utilizando redes neurais artificiais

This work describes the development of a nonlinear control strategy for an electro-hydraulic actuated system. The system to be controlled is represented by a third order ordinary differential equation subject to a dead-zone input. The control strategy is based on a nonlinear control scheme, combined with an artificial intelligence algorithm, namely, the method of feedback linearization and an artificial neural network. It is shown that, when such a hard nonlinearity and modeling inaccuracies are considered, the nonlinear technique alone is not enough to ensure a good performance of the controller. Therefore, a compensation strategy based on artificial neural networks, which have been notoriously used in systems that require the simulation of the process of human inference, is used. The multilayer perceptron network and the radial basis functions network as well are adopted and mathematically implemented within the control law. On this basis, the compensation ability considering both networks is compared. Furthermore, the application of new intelligent control strategies for nonlinear and uncertain mechanical systems are proposed, showing that the combination of a nonlinear control methodology and artificial neural networks improves the overall control system performance. Numerical results are presented to demonstrate the efficacy of the proposed control system

Utilização de redes neurais recorrentes na caracterização de cargas não lineares em sistemas elétricos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Formulação do controle preventivo em sistemas de distribuição de energia elétrica baseada na lógica fuzzy e redes neurais

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Desenvolvimento de um sistema para análise da estabilidade transitória de sistemas de energia elétrica via redes neurais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise de sensibilidade por redes neurais para estudos da estabilidade transitória de sistemas elétricos de potência

Pós-graduação em Engenharia Elétrica - FEIS

Análise de estabilidade transitória de sistemas elétricos por redes neurais ARTMAP nebulosas modulares

Pós-graduação em Engenharia Elétrica - FEIS

Nova metodologia para análise e síntese de sistemas de aterramento complexos utilizando o método lN-FDTD, computação paralela automática e redes neurais artificiais

Neste trabalho, o método FDTD em coordenadas gerais (LN-FDTD) foi implementado para a análise de estruturas de aterramento com geometrias coincidentes ou não com o sistema de coordenadas cartesiano. O método soluciona as equações de Maxwell no domínio do tempo, permitindo a obtenção de dados a respeito da resposta transitória e de regime estacionário de estruturas diversas de aterramento. Uma nova formulação para a técnica de truncagem UPML em coordenadas gerais, para meios condutivos, foi desenvolvida e implementada para viabilizar a análise dos problemas (LN-UPML). Uma nova metodologia baseada em duas redes neurais artificiais é apresentada para a deteccão de defeitos em malhas de terra. O software FDTD em coordenadas gerais foi testado e validado para vários casos. Uma interface gráfica para usuários, chamada LANE SAGS, foi desenvolvida para simplificar o uso e automatizar o processamento dos dados.