Identificação fuzzy-multimodelos para sistemas não lineares

This paper presents a new multi-model technique of dentification in ANFIS for nonlinear systems. In this technique, the structure used is of the fuzzy Takagi-Sugeno of which the consequences are local linear models that represent the system of different points of operation and the precursors are membership functions whose adjustments are realized by the learning phase of the neuro-fuzzy ANFIS technique. The models that represent the system at different points of the operation can be found with linearization techniques like, for example, the Least Squares method that is robust against sounds and of simple application. The fuzzy system is responsible for informing the proportion of each model that should be utilized, using the membership functions. The membership functions can be adjusted by ANFIS with the use of neural network algorithms, like the back propagation error type, in such a way that the models found for each area are correctly interpolated and define an action of each model for possible entries into the system. In multi-models, the definition of action of models is known as metrics and, since this paper is based on ANFIS, it shall be denominated in ANFIS metrics. This way, ANFIS metrics is utilized to interpolate various models, composing a system to be identified. Differing from the traditional ANFIS, the created technique necessarily represents the system in various well defined regions by unaltered models whose pondered activation as per the membership functions. The selection of regions for the application of the Least Squares method is realized manually from the graphic analysis of the system behavior or from the physical characteristics of the plant. This selection serves as a base to initiate the linear model defining technique and generating the initial configuration of the membership functions. The experiments are conducted in a teaching tank, with multiple sections, designed and created to show the characteristics of the technique. The results from this tank illustrate the performance reached by the technique in task of identifying, utilizing configurations of ANFIS, comparing the developed technique with various models of simple metrics and comparing with the NNARX technique, also adapted to identification

Análise e síntese de antenas e superfícies seletivas de frequência utilizando computação evolucionária e inteligência de enxames

The frequency selective surfaces, or FSS (Frequency Selective Surfaces), are structures consisting of periodic arrays of conductive elements, called patches, which are usually very thin and they are printed on dielectric layers, or by openings perforated on very thin metallic surfaces, for applications in bands of microwave and millimeter waves. These structures are often used in aircraft, missiles, satellites, radomes, antennae reflector, high gain antennas and microwave ovens, for example. The use of these structures has as main objective filter frequency bands that can be broadcast or rejection, depending on the specificity of the required application. In turn, the modern communication systems such as GSM (Global System for Mobile Communications), RFID (Radio Frequency Identification), Bluetooth, Wi-Fi and WiMAX, whose services are highly demanded by society, have required the development of antennas having, as its main features, and low cost profile, and reduced dimensions and weight. In this context, the microstrip antenna is presented as an excellent choice for communications systems today, because (in addition to meeting the requirements mentioned intrinsically) planar structures are easy to manufacture and integration with other components in microwave circuits. Consequently, the analysis and synthesis of these devices mainly, due to the high possibility of shapes, size and frequency of its elements has been carried out by full-wave models, such as the finite element method, the method of moments and finite difference time domain. However, these methods require an accurate despite great computational effort. In this context, computational intelligence (CI) has been used successfully in the design and optimization of microwave planar structures, as an auxiliary tool and very appropriate, given the complexity of the geometry of the antennas and the FSS considered. The computational intelligence is inspired by natural phenomena such as learning, perception and decision, using techniques such as artificial neural networks, fuzzy logic, fractal geometry and evolutionary computation. This work makes a study of application of computational intelligence using meta-heuristics such as genetic algorithms and swarm intelligence optimization of antennas and frequency selective surfaces. Genetic algorithms are computational search methods based on the theory of natural selection proposed by Darwin and genetics used to solve complex problems, eg, problems where the search space grows with the size of the problem. The particle swarm optimization characteristics including the use of intelligence collectively being applied to optimization problems in many areas of research. The main objective of this work is the use of computational intelligence, the analysis and synthesis of antennas and FSS. We considered the structures of a microstrip planar monopole, ring type, and a cross-dipole FSS. We developed algorithms and optimization results obtained for optimized geometries of antennas and FSS considered. To validate results were designed, constructed and measured several prototypes. The measured results showed excellent agreement with the simulated. Moreover, the results obtained in this study were compared to those simulated using a commercial software has been also observed an excellent agreement. Specifically, the efficiency of techniques used were CI evidenced by simulated and measured, aiming at optimizing the bandwidth of an antenna for wideband operation or UWB (Ultra Wideband), using a genetic algorithm and optimizing the bandwidth, by specifying the length of the air gap between two frequency selective surfaces, using an optimization algorithm particle swarm

Análise aerodinâmica de perfis de asa para aeronaves experimentais tipo jn-1

The great importance in selecting the profile of an aircraft wing concerns the fact that its relevance in the performance thereof; influencing this displacement costs (fuel consumption, flight level, for example), the conditions of flight safety (response in critical condition) of the plane. The aim of this study was to examine the aerodynamic parameters that affect some types of wing profile, based on wind tunnel testing, to determine the aerodynamic efficiency of each one of them. We compared three types of planforms, chosen from considerations about the characteristics of the aircraft model. One of them has a common setup, and very common in laboratory classes to be a sort of standard aerodynamic, it is a symmetrical profile. The second profile shows a conFiguration of the concave-convex type, the third is also a concave-convex profile, but with different implementation of the second, and finally, the fourth airfoil profile has a plano-convex. Thus, three different categories are covered in profile, showing the main points of relevance to their employment. To perform the experiment used a wind tunnel-type open circuit, where we analyzed the pressure distribution across the surface of each profile. Possession of the drag polar of each wing profile can be, from the theoretical basis of this work, the aerodynamic characteristics relate to the expected performance of the experimental aircraft, thus creating a selection model with guaranteed performance aerodynamics. It is believed that the philosophy used in this dissertation research validates the results, resulting in an experimental alternative for reliable implementation of aerodynamic testing in models of planforms

Cooperação em sagüis comuns callithrix jacchus: condições e restrições

The cooperative behavior is no longer a dilemma for the theory of evolution, since there are models that explain the evolution of this behavior by means of natural selection at the individual level. However, there have been few studies on the proximal factors that interfere with cooperative behavior. In the study of the influence of cognition on cooperation, many authors have been interested in situations in which individuals decide whether to act cooperatively and select partners with different qualities to cooperate. Of the factors studied, we highlight the need for understanding the apparatus and communication between partners to the occurrence of cooperation. Recently, highlight is the proposal that the ability to cooperate would be greater in species with cooperative breeding system. Thus, the common marmoset (Callithrix jacchus) is a New World monkey which stands as a valuable species for this type of study because it presents cooperative actions in nature, such as sharing food and protection of the community territory. Our experiment investigated whether common marmosets unrelated females (n = 6) were able to cooperate using an electrical and a mechanical apparatus, if this cooperation is a byproduct of individual actions or involve social attention, if it occurs inter-individual variation in the use of devices and formation of roles (producer / scrounger) in dyads. We use the number of rewards obtained by animals (Ratio of Correct Pulls) as indicators of cooperation and glances for partners (Ratio of Correct Glances) as indicators of social attention and communication. The results indicate that the type of apparatus was not a constraint for the occurrence of cooperation between the marmosets, but still has not been verified formation of roles in the dyads. The performance of animals in the two devices showed a large variation in the learning time, not having relationship with the performance in the tests phase. In both devices the level of social glances at control phases were casually correlated with any other phase, but the data showed that there was not social attention, that is, the monkeys realized that they gave food to the partners, but the partners did

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria