in this paper, the (m+1)-step adams-bashforth, adams-moulton, and predictor-correctormethods are used to solve rst-order linear fuzzy ordinary dierential equations. the conceptsof fuzzy interpolation and generalised strongly dierentiability are used, to obtaingeneral algorithms. each of these algorithms has advantages over current methods. moreover,for each algorithm a convergence formula can b...

In this paper, robustness to model uncertainties are analyzed in the context of discrete predictor-based statefeedback controllers for discrete-time input-delay systems with time-varying delay, in an LMI framework.The goal is comparing robustness of predictor-based strategies with respect to other (sub)optimal state feedback ones. A numerical example illustrates that improvements in tolerance t...

  Introduction: Social and organizational supports are effective factors closely related to job burnout and Due to the limited studies in this area on Iranian workers, this study aims to determine the predictability of social support and organizational support in  job burnout for workers in Yazd. Methods: This cross-sectional study was carried out among 200 textile workers in Yazd through str...

This paper investigates the relation between an error distribution and a predictive order of minimum mean abusolute error predictors(MMAE predictors) designed for lossless coding of greyscale images. Design of MMAE predictors reduces to the linear programming problem. Let k is the number of coefficients in a predictor(predictor order), we imagine that predictor order k have a distribution shapi...

Linear discriminant analysis with binary response is considered when the predictor is a functional random variableX = {Xt, t ∈ [0, T ]}, T ∈ R. Motivated by a food industry problem, we develop a methodology to anticipate the prediction by determining the smallest T ∗, T ∗ ≤ T , such that X∗ = {Xt, t ∈ [0, T ∗]} and X give similar predictions. The adaptive prediction concerns the observation of ...

This paper concerns the problem of the control of linear systems by means of feedback from delayed output, where the delay is known and time-varying. The main advantage of the approach is that it can be applied to systems with any delay bound, i.e. not only small delays. The predictor is based on a combination of finite-dimensional elementary predictors whose number can be suitably chosen to co...

This paper considers the output regulation problem for linear systems in the presence of state, input and output delays. A state feedback output regulation law is constructed from a state predictor, recently developed for systems with state and input delays, and the solution to a pair of regulator equations that transforms the output regulation problem into a stabilization problem. Necessary an...

– This paper applies neural network methodology to inßation forecasting in the Euro-area and the USA. Neural network methodology outperforms linear forecasting methods for the Euro Area at forecast horizons of one, three, and six month horizons, while the linear model is preferable for US data. The nonlinear estimation shows that unemployment is a signiÞcant predictor of inßation for the Euro A...

An improved homoclinic predictor at a generic codim 2 Bogdanov-Takens (BT) bifucation is derived. We use the classical “blow-up” technique to reduce the canonical smooth normal form near a generic BT bifurcation to a perturbed Hamiltonian system. With a simple perturbation method, we derive explicit firstand second-order corrections of the unperturbed homoclinic orbit and parameter value. To ob...

Some new results of a distance{based (DB) model for prediction with mixed variables are presented and discussed. This model can be thought of as a linear model where predictor variables for a response Y are obtained from the observed ones via classic mul-tidimensional scaling. A coeecient is introduced in order to choose the most predictive dimensions, providing a solution to the problem of sma...