The Vector Autoregressive Moving Average (VARMA) Model is used to identify dynamical characteristics of a structural system in the presence of noise. In order to estimate the parameters of the VARMA Model, the Spliid’s fast algorithm is used. To determine the modal parameters the companion matrix is built with the autoregressive part of the VARMA Model. The performance of this method here discussed is presented by means of simulations, using three degrees of freedom mass-dampingstiffness vibrating system.

A model to measure objectively the probability preferably having the public for a soft drink, the variable to be measured is sensory, ie is a subjective variable, in our case the flavor is presented in preente thesis, for Kola Real, Coca Cola and Pepsi Cola: organoleptic study, three sodas were selected. The advantage of the model presented is grouped qye soda in pairs, each pair consisting of a group of respondents was presented which showed individual preference or indifference sodas presented in par.

The Vector Autoregressive Moving Average (VARMA) Model is used to identify dynamical characteristics of a structural system in the presence of noise. In order to estimate the parameters of the VARMA Model, the Spliid’s fast algorithm is used. To determine the modal parameters the companion matrix is built with the autoregressive part of the VARMA Model. The performance of this method here discussed is presented by means of simulations, using three degrees of freedom mass-dampingstiffness vibrating system.

A model to measure objectively the probability preferably having the public for a soft drink, the variable to be measured is sensory, ie is a subjective variable, in our case the flavor is presented in preente thesis, for Kola Real, Coca Cola and Pepsi Cola: organoleptic study, three sodas were selected. The advantage of the model presented is grouped qye soda in pairs, each pair consisting of a group of respondents was presented which showed individual preference or indifference sodas presented in par.

The article presents a Parser class in Java to evaluate algebraic expressions using fundamental algorithms for the construction of compilers, passing from the conversion of expressions from infix to postfix, the evaluation of postfix expressions, the evaluation for precedence of operators, parser algorithms of precedence and construction algorithm of precedence functions. The goal of the article is to write a lexical analyser in a conventional language of systems programming, using the possibilities of input and output of Java language in order to read expressions to evaluate from the input, to process them and to send the results to the output.

The article presents a Parser class in Java to evaluate algebraic expressions using fundamental algorithms for the construction of compilers, passing from the conversion of expressions from infix to postfix, the evaluation of postfix expressions, the evaluation for precedence of operators, parser algorithms of precedence and construction algorithm of precedence functions. The goal of the article is to write a lexical analyser in a conventional language of systems programming, using the possibilities of input and output of Java language in order to read expressions to evaluate from the input, to process them and to send the results to the output.