Nonlinear partial diferential equations are a class of partial diferential equations having many important uses in engineering and sciences. In this work we display a comparison between Adomian Decomposition Method (ADM) and Differential Quadrature Method (DQM) for solving some nonlinear partial diferential equations. We found the existence of exact solutions for those models. The numerical res...

In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to such sets. Finally, a conflict resolving method is used to combine partial answers from classifiers to obtain final classificat...

Projection to latent structures or partial least squares (PLS) produces output-supervised decomposition on inputX, while principal component analysis (PCA) produces unsupervised decomposition of inputX. In this paper, the effect of outputYon theX-space decomposition in PLS is analyzed andgeometric properties of the PLS structure are revealed. Several PLS algorithms are compared in a geometric w...

Reconstruction of the Pressure in Method Asymptotic Partial Decomposition for Flows Tube Structures

In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to such sets. Finally, a conflict resolving method is used to combine partial answers from classifiers to obtain final classificat...

This paper presents the findings of an investigation into Partial Discharge signal denoising using techniques based on Empirical Mode Decomposition. The denoising techniques are based on thresholding the Intrinsic Mode Functions which result from the Empirical Mode Decomposition of a signal. The results of the tests carried out show clearly that these techniques can produce excellent results wh...

Adaptive decomposition of complex data is realized and intrinsic mode function (IMF) components that reflect different scales information are gained through empirical mode decomposition (EMD) of partial discharge (PD) signals. The gained intrinsic mode function components are reconstructed after the wavelet threshold processing to reduce the interference of noise. This partial discharge signals...

In this paper, we will present a comparison between the Adomian Decomposition Method (ADM) and Taylor Matrix Method by solving some well-known partial differential equations (PDEs). In order to illustrate the analysis we examined the Telegraph equation, which is considered one of the most significant partial differential equations, describe wave propagation of electric signals in a cable transm...

We present a dimension adaptive sparse grid combination technique for the machine learning problem of regression. A function over a d-dimensional space, which assumedly describes the relationship between the features and the response variable, is reconstructed using a linear combination of partial functions; these may depend only on a subset of all features. The partial functions, which are pie...

Loewy’s decomposition of a linear ordinary differential operator as the product of largest completely reducible components is generalized to partial differential operators of order three in two variables. This is made possible by considering the problem in the ring of partial differential operators where both left intersections and right divisors of left ideals are not necessarily principal. Li...