Eficiência e razão de hedge: uma análise dos mercados futuro brasileiros de boi, café, etanol, milho e soja

This research aims to investigate the Hedge Efficiency and Optimal Hedge Ratio for the future market of cattle, coffee, ethanol, corn and soybean. This paper uses the Optimal Hedge Ratio and Hedge Effectiveness through multivariate GARCH models with error correction, attempting to the possible phenomenon of Optimal Hedge Ratio differential during the crop and intercrop period. The Optimal Hedge Ratio must be bigger in the intercrop period due to the uncertainty related to a possible supply shock (LAZZARINI, 2010). Among the future contracts studied in this research, the coffee, ethanol and soybean contracts were not object of this phenomenon investigation, yet. Furthermore, the corn and ethanol contracts were not object of researches which deal with Dynamic Hedging Strategy. This paper distinguishes itself for including the GARCH model with error correction, which it was never considered when the possible Optimal Hedge Ratio differential during the crop and intercrop period were investigated. The commodities quotation were used as future price in the market future of BM&FBOVESPA and as spot market, the CEPEA index, in the period from May 2010 to June 2013 to cattle, coffee, ethanol and corn, and to August 2012 to soybean, with daily frequency. Similar results were achieved for all the commodities. There is a long term relationship among the spot market and future market, bicausality and the spot market and future market of cattle, coffee, ethanol and corn, and unicausality of the future price of soybean on spot price. The Optimal Hedge Ratio was estimated from three different strategies: linear regression by MQO, BEKK-GARCH diagonal model, and BEKK-GARCH diagonal with intercrop dummy. The MQO regression model, pointed out the Hedge inefficiency, taking into consideration that the Optimal Hedge presented was too low. The second model represents the strategy of dynamic hedge, which collected time variations in the Optimal Hedge. The last Hedge strategy did not detect Optimal Hedge Ratio differential between the crop and intercrop period, therefore, unlikely what they expected, the investor do not need increase his/her investment in the future market during the intercrop

Estudo e implementação de um controlador preditivo generalizado bilinear compensado adaptativo

The present work presents the study and implementation of an adaptive bilinear compensated generalized predictive controller. This work uses conventional techniques of predictive control and includes techniques of adaptive control for better results. In order to solve control problems frequently found in the chemical industry, bilinear models are considered to represent the dynamics of the studied systems. Bilinear models are simpler than general nonlinear model, however it can to represent the intrinsic not-linearities of industrial processes. The linearization of the model, by the approach to time step quasilinear , is used to allow the application of the equations of the generalized predictive controller (GPC). Such linearization, however, generates an error of prediction, which is minimized through a compensation term. The term in study is implemented in an adaptive form, due to the nonlinear relationship between the input signal and the prediction error.Simulation results show the efficiency of adaptive predictive bilinear controller in comparison with the conventional.

Espectroscopia no infravermelho próximo e métodos de calibração multivariada aplicados à determinação simultânea de parâmetros bioquímicos em plasma sanguíneo

In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

Estudos de adsorção de tetraciclina em partículas de quitosana

Due to its physico-chemical and biological properties, related to the abundance and low cost of raw material, chitosan has been recognized as a material of wide application in various fields, such as in drug delivery systems. Many of these properties are associated with the presence of amino groups in its polymer chain. A proper determination of these amino groups is very important, in order to properly specify if a given chitosan sample can be used in a particular application. Thus, in this work, initially, a comparison between the determination of the deacetylation degree by conductometry and elemental analysis was carried out using a detailed analysis of error propagation. It was shown that the conductometric analysis resulted in a simple and safe method for the determining the degree of deacetylation of chitosan. Subsequently, experiments were performed to monitor and characterize the adsorption of tetracycline on chitosan particles through kinetic and equilibrium studies. The main models of kinetics and adsorption isotherms, widely used to describe the adsorption on wastewater treatment systems and the drug loading, were used to treat the experimental data. Firstly, it was shown that an apparent linear t/q(t) × t relationship did not imply in a pseudo-second-order adsorption kinetics, differently of what has been repeatedly reported in the literature. It was found that this misinterpretation can be avoided by using non-linear regression. Finally, the adsorption of tetracycline on chitosan particles was analyzed using insights obtained from theoretical analysis, and the parameters generated were used to analyze the kinetics of adsorption, the isotherm of adsorption and to ropose a mechanism of adsorption

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.

Eficiência e razão de hedge: uma análise dos mercados futuro brasileiros de boi, café, etanol, milho e soja

This research aims to investigate the Hedge Efficiency and Optimal Hedge Ratio for the future market of cattle, coffee, ethanol, corn and soybean. This paper uses the Optimal Hedge Ratio and Hedge Effectiveness through multivariate GARCH models with error correction, attempting to the possible phenomenon of Optimal Hedge Ratio differential during the crop and intercrop period. The Optimal Hedge Ratio must be bigger in the intercrop period due to the uncertainty related to a possible supply shock (LAZZARINI, 2010). Among the future contracts studied in this research, the coffee, ethanol and soybean contracts were not object of this phenomenon investigation, yet. Furthermore, the corn and ethanol contracts were not object of researches which deal with Dynamic Hedging Strategy. This paper distinguishes itself for including the GARCH model with error correction, which it was never considered when the possible Optimal Hedge Ratio differential during the crop and intercrop period were investigated. The commodities quotation were used as future price in the market future of BM&FBOVESPA and as spot market, the CEPEA index, in the period from May 2010 to June 2013 to cattle, coffee, ethanol and corn, and to August 2012 to soybean, with daily frequency. Similar results were achieved for all the commodities. There is a long term relationship among the spot market and future market, bicausality and the spot market and future market of cattle, coffee, ethanol and corn, and unicausality of the future price of soybean on spot price. The Optimal Hedge Ratio was estimated from three different strategies: linear regression by MQO, BEKK-GARCH diagonal model, and BEKK-GARCH diagonal with intercrop dummy. The MQO regression model, pointed out the Hedge inefficiency, taking into consideration that the Optimal Hedge presented was too low. The second model represents the strategy of dynamic hedge, which collected time variations in the Optimal Hedge. The last Hedge strategy did not detect Optimal Hedge Ratio differential between the crop and intercrop period, therefore, unlikely what they expected, the investor do not need increase his/her investment in the future market during the intercrop

Crescimento econômico e restrição externa: evidências empíricas na economia brasileira (1991-2010)

This study aims to investigate the influence of the balance of payments constrained on economic growth in Brazil from 1991 to 2010. With this order, are shown some of the Keynesian balance of payments constrained growth models, inspired by Thirlwall (1979) and Kaldor (1970), which are supported by important points in common, such as adherence to the principle of effective demand. Given that within this theoretical perspective, there is no consensus about the best model to explain the growth rate allowed by the balance of payments constraint, the results are presented by the representative of the empirical literature that addresses the topic, which are necessary for understand the Brazilian case. From the estimation of the income elasticity of imports (0.85) via autoregressive vectors with error correction (VEC), it was calculated five growth rates of income, as predicted by the models of Thirlwall (1979), Thirlwall and Hussain (1982), Moreno-Brid (1998, 2003) and Lourenço et al. (2011) and compared with the actual growth rate. The empirical analysis has shown that: it can not reject the presence of external constraint in the Brazilian economy, there is a strong similarity in growth rates provided by different modeling suggest that growth with external constraint. In addition, when using data in quarterly for the period after 1990 there are no factors that could cause instability in the parameters of the import function (income elasticity and price elasticity of imports) within the period, which indicates that the structural break widely associated with the year 1994 was not confirmed by this study

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

Identificação em tempo real de modelo dinâmico de robô móvel com acionamento diferencial e zona morta

Several mobile robots show non-linear behavior, mainly due friction phenomena between the mechanical parts of the robot or between the robot and the ground. Linear models are efficient in some cases, but it is necessary take the robot non-linearity in consideration when precise displacement and positioning are desired. In this work a parametric model identification procedure for a mobile robot with differential drive that considers the dead-zone in the robot actuators is proposed. The method consists in dividing the system into Hammerstein systems and then uses the key-term separation principle to present the input-output relations which shows the parameters from both linear and non-linear blocks. The parameters are then simultaneously estimated through a recursive least squares algorithm. The results shows that is possible to identify the dead-zone thresholds together with the linear parameters

Modelagem linear e identificação do modelo dinâmico de um robô móvel com acionamento diferencial

This work presents a modelling and identification method for a wheeled mobile robot, including the actuator dynamics. Instead of the classic modelling approach, where the robot position coordinates (x,y) are utilized as state variables (resulting in a non linear model), the proposed discrete model is based on the travelled distance increment Delta_l. Thus, the resulting model is linear and time invariant and it can be identified through classical methods such as Recursive Least Mean Squares. This approach has a problem: Delta_l can not be directly measured. In this paper, this problem is solved using an estimate of Delta_l based on a second order polynomial approximation. Experimental data were colected and the proposed method was used to identify the model of a real robot

Controlador preditivo não linear aplicado ao controle de golfadas em processos de produção de petróleo

Slugging is a well-known slugging phenomenon in multiphase flow, which may cause problems such as vibration in pipeline and high liquid level in the separator. It can be classified according to the place of its occurrence. The most severe, known as slugging in the riser, occurs in the vertical pipe which feeds the platform. Also known as severe slugging, it is capable of causing severe pressure fluctuations in the flow of the process, excessive vibration, flooding in separator tanks, limited production, nonscheduled stop of production, among other negative aspects that motivated the production of this work . A feasible solution to deal with this problem would be to design an effective method for the removal or reduction of the system, a controller. According to the literature, a conventional PID controller did not produce good results due to the high degree of nonlinearity of the process, fueling the development of advanced control techniques. Among these, the model predictive controller (MPC), where the control action results from the solution of an optimization problem, it is robust, can incorporate physical and /or security constraints. The objective of this work is to apply a non-conventional non-linear model predictive control technique to severe slugging, where the amount of liquid mass in the riser is controlled by the production valve and, indirectly, the oscillation of flow and pressure is suppressed, while looking for environmental and economic benefits. The proposed strategy is based on the use of the model linear approximations and repeatedly solving of a quadratic optimization problem, providing solutions that improve at each iteration. In the event where the convergence of this algorithm is satisfied, the predicted values of the process variables are the same as to those obtained by the original nonlinear model, ensuring that the constraints are satisfied for them along the prediction horizon. A mathematical model recently published in the literature, capable of representing characteristics of severe slugging in a real oil well, is used both for simulation and for the project of the proposed controller, whose performance is compared to a linear MPC

Controle por modos deslizantes com compensação difusa aplicado a sistemas com descontinuidade

The development of non-linear controllers gained space in the theoretical ambit and of practical applications on the moment that the arising of digital computers enabled the implementation of these methodologies. In comparison with the linear controllers more utilized, the non -linear controllers present the advantage of not requiring the linearity of the system to determine the parameters of control, which permits a more efficient control especially when the system presents a high level of non-linearity. Another additional advantage is the reduction of costs, since to obtain the efficient control through linear controllers it is necessary the utilization of sensors and more refined actuators than when it is utilized a non-linear controller. Among the non-linear theories of control, the method of control by gliding ways is detached for being a method that presents more robustness, before uncertainties. It is already confirmed that the adoption of compensation on the region of residual error permits to improve better the performance of these controllers. So, in this work it is described the development of a non-linear controller that looks for an association of strategy of control by gliding ways, with the fuzzy compensation technique. Through the implementation of some strategies of fuzzy compensation, it was searched the one which provided the biggest efficiency before a system with high level of nonlinearities and uncertainties. The electrohydraulic actuator was utilized as an example of research, and the results appoint to two configurations of compensation that permit a bigger reduction of the residual error