We compare parametric and machine learning techniques (namely: Neural Networks) for in–sample modeling of the yield curve BRICS countries (Brazil, Russia, India, China, South Africa). To such aim, we applied Dynamic De Rezende–Ferreira five–factor model with time–varying decay parameters a Feed–Forward Network to bond market data countries. enhance flexibility model, also introduce new procedur...

The 13C chemical shifts for a series of compounds which includes carboxylic acids, aldehydes, ethers, ketones and hydrocarbons were simulated by using the parametric techniques. The observed chemical shifts were related to numerically encoded structural parametes called descriptors. Two new electronic descriptors were added to the previous descriptors. Ploting of experimental ver...

We present a new technique, which we call ‘texture unmapping’, and its application to image reconstruction. A parametric model of an image is made, and then subtracted from the image to leave a non-parametric component. The two components may then be reconstructed independently and then recombined to give a final reconstruction. Results of the new technique are given on a range of images.

Electroacoustic devices or systems transfer functions are usually described by means of amplitude and phase Bode plots. They are obtained from usually large data series of excitation and response values. Acoustic set-up requirements vary considerably, from normal room to anechoic conditions, from complex instrumentation to simple sweeping ensembles. Filter frequency analysis, cross-spectral est...

Symbolic preprocessing techniques are very useful to obtain low order LFT-representations for parametric models. In this paper we give an overview about existing preprocessing methods and we present new techniques and enhancements of existing methods. All methods are implemented in the new version 2 of the LFR-toolbox and their capabilities are illustrated by a challenging aircraft parametric u...

Over the last few decades, the investigation of ultrafast phenomena occurring in atoms, molecules and solid-state systems under a strong-field regime of light-matter interaction has attracted great attention. The increasing request for a suitable optical technology is significantly boosting the development of powerful ultrafast laser sources. In this framework, Optical Parametric Amplification ...

In this work, we get focused on the use of statistical techniques for behavior analysis of Artificial Neural Networks in the task of classification. A study of the non-parametric tests use is presented, using some well-known models of neural networks. The results show the need of using non-parametric statistic, because the Artificial Neural Networks used do not verify the hypothesis required fo...

| This research deals with developing automatic classiication algorithms for identifying images of F-actin distribution in endothelial cells with diierent treatment of agonists. Parametric and non-parametric classiication techniques were investigated such as statistical and artiicial neu-ral network classiiers. First and second order features were extracted from the images. Among the statistica...