Otimização de superfícies seletivas de frequência com elementos pré-fractais utilizando rede neural MLP e algoritmos de busca populacional

This thesis describes design methodologies for frequency selective surfaces (FSSs) composed of periodic arrays of pre-fractals metallic patches on single-layer dielectrics (FR4, RT/duroid). Shapes presented by Sierpinski island and T fractal geometries are exploited to the simple design of efficient band-stop spatial filters with applications in the range of microwaves. Initial results are discussed in terms of the electromagnetic effect resulting from the variation of parameters such as, fractal iteration number (or fractal level), fractal iteration factor, and periodicity of FSS, depending on the used pre-fractal element (Sierpinski island or T fractal). The transmission properties of these proposed periodic arrays are investigated through simulations performed by Ansoft DesignerTM and Ansoft HFSSTM commercial softwares that run full-wave methods. To validate the employed methodology, FSS prototypes are selected for fabrication and measurement. The obtained results point to interesting features for FSS spatial filters: compactness, with high values of frequency compression factor; as well as stable frequency responses at oblique incidence of plane waves. This thesis also approaches, as it main focus, the application of an alternative electromagnetic (EM) optimization technique for analysis and synthesis of FSSs with fractal motifs. In application examples of this technique, Vicsek and Sierpinski pre-fractal elements are used in the optimal design of FSS structures. Based on computational intelligence tools, the proposed technique overcomes the high computational cost associated to the full-wave parametric analyzes. To this end, fast and accurate multilayer perceptron (MLP) neural network models are developed using different parameters as design input variables. These neural network models aim to calculate the cost function in the iterations of population-based search algorithms. Continuous genetic algorithm (GA), particle swarm optimization (PSO), and bees algorithm (BA) are used for FSSs optimization with specific resonant frequency and bandwidth. The performance of these algorithms is compared in terms of computational cost and numerical convergence. Consistent results can be verified by the excellent agreement obtained between simulations and measurements related to FSS prototypes built with a given fractal iteration

Separação cega de fontes lineares e não lineares usando algoritmo genético, redes neurais artificiais RBF e negentropia de Rényi como medida de independência

Conventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and Rényi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations

Critério de correntropia no treinamento de redes fuzzy wavelet neural networks para identificação de sistemas dinâmicos não lineares

The great interest in nonlinear system identification is mainly due to the fact that a large amount of real systems are complex and need to have their nonlinearities considered so that their models can be successfully used in applications of control, prediction, inference, among others. This work evaluates the application of Fuzzy Wavelet Neural Networks (FWNN) to identify nonlinear dynamical systems subjected to noise and outliers. Generally, these elements cause negative effects on the identification procedure, resulting in erroneous interpretations regarding the dynamical behavior of the system. The FWNN combines in a single structure the ability to deal with uncertainties of fuzzy logic, the multiresolution characteristics of wavelet theory and learning and generalization abilities of the artificial neural networks. Usually, the learning procedure of these neural networks is realized by a gradient based method, which uses the mean squared error as its cost function. This work proposes the replacement of this traditional function by an Information Theoretic Learning similarity measure, called correntropy. With the use of this similarity measure, higher order statistics can be considered during the FWNN training process. For this reason, this measure is more suitable for non-Gaussian error distributions and makes the training less sensitive to the presence of outliers. In order to evaluate this replacement, FWNN models are obtained in two identification case studies: a real nonlinear system, consisting of a multisection tank, and a simulated system based on a model of the human knee joint. The results demonstrate that the application of correntropy as the error backpropagation algorithm cost function makes the identification procedure using FWNN models more robust to outliers. However, this is only achieved if the gaussian kernel width of correntropy is properly adjusted.

Viabilidade técnica de uma rota não convencional para a produção de leite de cabra em pó em cooperativas do Rio Grande do Norte

Nowadays, Brazil has both the greatest goat herd and the greatest goat milk production of South America. The state of Rio Grande do Norte, located in northeast of Brazil, has an average year production of three thousand cubic meters of goat milk in natura. Part of this milk production is homemade and it comes from small farms, which unite in rural cooperatives created to encourage the production and implementation of industrial processes for preservation and processing of milk. Results presented by literature and obtained from preliminary essays in this thesis show that non conventional dryer of spouted bed with inert particles is able to produce powder milk from in natura milk (cattle or goat), with the same quality of spray dryer, however, operating at low cost. The method of drying in spouted bed consists of injecting milk emulsion on the bed of inert particles gushed by hot air. This emulsion covers the particles with a thin film, which dries and is reduced to powder during the circulation of inerts inside the bed. The powder is dragged by exhaustion air and separated in the cyclone. The friction among particles resulted from the particles circulation, encourages high taxes of shear in the thin film of emulsion, breaking the cohesive forces and making this process possible. Studying the drying process and the powder goat milk production in one unit of spouted bed with inert particles, seeing the development of a low cost technological route for powder milk production is the aim of this thesis. The powder milk produced by this route must attend the local demand of food industries which need an intermediate product to be used as a food ingredient (ice-cream, milk candy). In order to reach this aim, this thesis approaches the aspects related to physical, thermodynamics and physic-chemicals characteristics of goat milk, whose complete data are still inexistent in the literature. The properties of materials are of great importance to the project of any process which involves the operations of transportation of movement, heat and mass quantity, such as the dryers which operate in fluid dynamically active regime, like the spouted bed. It was obtained new data related to the goat milk properties in function of concentration of solids and temperature. It is also important to mention the study developed about the kinetic of solids retention in the bed of inert particles during the drying of goat milk. It was found more adequate processes conditions to the proposed technological route to be implemented in small and micro-industries, with simplifications in the system of milk injection as well as in the form of operation of the dryer. Important data were obtained for a posterior stage of this research which involves the v modeling, simulation, control and optimization of the process. The results obtained in this thesis, in relation to process performance as well as to the quality of produced powder milk validate the proposal of using the spouted bed dryer in the production of powder goat milk