Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

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.

Human face verification based on multidimensional Polynomial Powers of Sigmoid (PPS)

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.

Synthesis and thermal characterization of luminescent powers of terbium(III) with mixed ligands

This work deals with the synthesis and thermal decomposition of complexes of general formula: Ln(beta-dik)(3)L (where Ln=Tb(+3), beta-dik=4,4,4-trifluoro-1-phenyl-1,3butanedione(btfa) and L=1,10-fenantroline(phen) or 2,2-bipiridine(bipy). The powders were characterized by melting point, FTIR spectroscopy, LTV-visible, elemental analysis, scanning differential calorimeter(DSC) and thermogravimetry(TG). The TG/DSC curves were obtained simultaneously in a system DSC-TGA, under nitrogen atmosphere. The experimental conditions were: 0.83 ml.s(-1) carrier gas flow, 2.0 +/- 0.5 mg samples and 10 degrees C.min(-1) heating rate. The CHN elemental analysis of the Tb(btfa)(3)bipy and Tb(btfa)(3)phen complexes, are in good agreement with the expected values. The IR spectra evinced that the metal ion is coordinated to the ligands via C=O and C-N groups. The TG/DTG/DSC curves of the complexes show that they decompose before melting. The profiles of the thermal decomposition of the Tb(btfa)3phen and Tb(btfa)3bipy showed six and five decomposition stages, respectively. Our data suggests that the thermal stability of the complexes under investigation followed the order: Tb(btfa)(3)phen < Tb(btfa)(3)bipy.

Emerging powers, humanitarian assistance and foreign policy: the case of Brazil during the earthquake crisis in Haiti

During the management of the crisis after the earthquake occurred in Haiti in 12 January 2010, Brazil played an important role on the efforts of humanitarian assistance. Based on bibliography and documents the paper presents the role played by Brazil with the focus on the humanitarian assistance as part of country’ foreign policy as an emerging power to increase the presence into the international system. To achieve this goal the article presents some considerations about emerging powers, foreign policy and theoretical concepts about humanitarian assistance and international relations, the extension of the earthquake in Haiti and the actions performed by Brazil during the response phase of the crisis management.

Learning kernels for support vector machines with polynomial powers of sigmoid

In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.

Comparative study between powers of sigmoid functions, MLP-backpropagation and polynomials in function approximation problems

Function approximation is a very important task in environments where the computation has to be based on extracting information from data samples in real world processes. So, the development of new mathematical model is a very important activity to guarantee the evolution of the function approximation area. In this sense, we will present the Polynomials Powers of Sigmoid (PPS) as a linear neural network. In this paper, we will introduce one series of practical results for the Polynomials Powers of Sigmoid, where we will show some advantages of the use of the powers of sigmiod functions in relationship the traditional MLP-Backpropagation and Polynomials in functions approximation problems.