78 resultados para Multidimensional Variable

em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"


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Organisms are constantly subjected to stressful stimuli that affect numerous physiological processes and activate the hypothalamo-pituitary-adrenal (HPA) axis, increasing the release of glucocorticoids. Exposure to chronic stress is known to alter basic mechanisms of the stress response. The purpose of the present study was to compare the effect of two different stress paradigms (chronic restraint or variable stress) on behavioral and corticosterone release to a subsequent exposure to stressors. Considering that the HPA axis might respond differently when it is challenged with a novel or a familiar stressor we investigated the changes in the corticosterone levels following the exposure to two stressors: restraint (familiar stress) or forced novelty (novel stress). The changes in the behavioral response were evaluated by measuring the locomotor response to a novel environment. In addition, we examined changes in body, adrenals, and thymus weights in response to the chronic paradigms. Our results showed that exposure to chronic variable stress increased basal plasma corticosterone levels and that both, chronic restraint and variable stresses, promote higher corticosterone levels in response to a novel environment, but not to a challenge restraint stress, as compared to the control (non-stressed) group. Exposure to chronic restraint leads to increased novelty-induced locomotor activity. Furthermore, only the exposure to variable stress reduced body weights. In conclusion, the present results provide additional evidence on how chronic stress affects the organism physiology and point to the importance of the chronic paradigm and challenge stress on the behavioral and hormonal adaptations induced by chronic stress. (c) 2006 Elsevier B.V. All rights reserved.

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Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

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In this paper, we described how a multidimensional wavelet neural networks based on Polynomial Powers of Sigmoid (PPS) can be constructed, trained and applied in image processing tasks. In this sense, a novel and uniform framework for face verification is presented. The framework is based on a family of PPS wavelets,generated from linear combination of the sigmoid functions, and can be considered appearance based in that features are extracted from the face image. The feature vectors are then subjected to subspace projection of PPS-wavelet. The design of PPS-wavelet neural networks is also discussed, which is seldom reported in the literature. The Stirling Universitys face database were used to generate the results. Our method has achieved 92 % of correct detection and 5 % of false detection rate on the database.

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It is shown that, in the two brane time variation model framework, if the hidden brane tension varies according to the phenomenological Eotvos law, the visible brane tension behavior is such that its time derivative is negative in the past and positive after a specific time of cosmological evolution. This behavior is interpreted in terms of a useful mechanical system analog and its relation with the variation of the Newtonian (effective) gravitational constant is explored.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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The general assumption under which the (X) over bar chart is designed is that the process mean has a constant in-control value. However, there are situations in which the process mean wanders. When it wanders according to a first-order autoregressive (AR (1)) model, a complex approach involving Markov chains and integral equation methods is used to evaluate the properties of the (X) over bar chart. In this paper, we propose the use of a pure Markov chain approach to study the performance of the (X) over bar chart. The performance of the chat (X) over bar with variable parameters and the (X) over bar with double sampling are compared. (C) 2011 Elsevier B.V. All rights reserved.

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Continuous-time neural networks for solving convex nonlinear unconstrained;programming problems without using gradient information of the objective function are proposed and analyzed. Thus, the proposed networks are nonderivative optimizers. First, networks for optimizing objective functions of one variable are discussed. Then, an existing one-dimensional optimizer is analyzed, and a new line search optimizer is proposed. It is shown that the proposed optimizer network is robust in the sense that it has disturbance rejection property. The network can be implemented easily in hardware using standard circuit elements. The one-dimensional net is used as a building block in multidimensional networks for optimizing objective functions of several variables. The multidimensional nets implement a continuous version of the coordinate descent method.

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A novel single-phase voltage source rectifier capable to achieve High-Power-Factor (HPF) for variable speed refrigeration system application, is proposed in this paper. The proposed system is composed by a single-phase high-power-factor boost rectifier, with two cells in interleave connection, operating in critical conduction mode, and employing a soft-switching technique, controlled by a Field Programmable Gate Array (FPGA), associated with a conventional three-phase IGBT bridge inverter (VSI - Voltage Source Inverter), controlled by a Digital Signal Processor (DSP). The soft-switching technique for the input stage is based on zero-current-switching (ZCS) cells. The rectifier's features include the reduction in the input current ripple, the reduction in the output voltage ripple, the use of low stress devices, low volume for the EMI input filter, high input power factor (PF), and low total harmonic distortion (THD) in the input current, in compliance with the EEC61000-3-2 standards. The digital controller for the output stage has been developed using a conventional voltage-frequency control (scalar V/f control), and a simplified stator oriented Vector control, in order to verify the feasibility and performance of the proposed digital controls for continuous temperature control applied at a refrigerator prototype.

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