Mathematical tests about the existence and applications of PPS-wavelets in function approximation problems

Neural networks and wavelet transform have been recently seen as attractive tools for developing eficient solutions for many real world problems in function approximation. Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. So, mathematical model is a very important tool to guarantee the development of the neural network area. In this article we will introduce one series of mathematical demonstrations that guarantee the wavelets properties for the PPS functions. As application, we will show the use of PPS-wavelets in pattern recognition problems of handwritten digit through function approximation techniques.

Comparative study between RBF and radial-PPS neural networks

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.

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.

Efeito da degradação ambiental nas propriedades de cisalhamento de compósitos PPS/fibra de carbono

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Análise do efeito higrotérmico no comportamento em fadiga de compósitos de PPS/fibras de carbono

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Feedforward neural networks based on PPS-wavelet activation functions

Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. Neural networks and wavenets have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. In this paper, it is shown how feedforward neural networks can be built using a different type of activation function referred to as the PPS-wavelet. An algorithm is presented to generate a family of PPS-wavelets that can be used to efficiently construct feedforward networks for function approximation.

The effect of the ocean water immersion and UV ageing on the dynamic mechanical properties of the PPS/glass fiber composites

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Determination of density of states in PPS from space-charge-limited current measurements

Space-charge-limited currents measurements have been carried out on undoped amorphous poly p-phenylene sulfide. The scaling law is checked for different samples with varying thickness, and J-V data analyzed. The position of the quasi-Fermi level and the density of states was obtained.

Avaliação dos parâmetros de soldagem para compósitos de PPS/fibras contínuas com aplicações aeronáuticas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Efeito do condicionamento ambiental em compósitos soldados de PPS/fibras contínuas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo dos critérios de falhas em compósitos PPS/fibras de carbono utilizando elementos finitos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação do efeito higrotérmico nas propriedades mecânicas de compósitos de PPS/fibras contínuas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estudo das intempéries nas propriedades mecânicas e viscoelásticas de compósitos PPS/Fibras de carbono e EPOXI/Fibras de carbono de uso aeronaútico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo da modificação superficial de fibras de carbono por meio de tratamentos a plasma para o aumento da adesão na interface de compósitos fibra de carbono/PPS

Pós-graduação em Engenharia Mecânica - FEG