Choice of free parameters in expansions of discrete-time Volterra models using Kautz functions

Automatica ◽  
2007 ◽  
Vol 43 (6) ◽  
pp. 1084-1091 ◽  
Author(s):  
Alex da Rosa ◽  
Ricardo J.G.B. Campello ◽  
Wagner C. Amaral
Keyword(s):  
Discrete Time ◽  
Volterra Models ◽  
Kautz Functions ◽  
Free Parameters

CHOICE OF FREE PARAMETERS IN EXPANSIONS OF DISCRETE-TIME VOLTERRA MODELS USING KAUTZ FUNCTIONS

2005 ◽  
Vol 38 (1) ◽  
pp. 761-766 ◽  
Author(s):  
Alex da Rosa ◽  
Wagner C. Amaral ◽  
Ricardo J.G.B. Campello
Keyword(s):  
Discrete Time ◽  
Volterra Models ◽  
Kautz Functions ◽  
Free Parameters

Optimal expansions of discrete-time Volterra models using Laguerre functions

Automatica ◽  
2004 ◽  
Vol 40 (5) ◽  
pp. 815-822 ◽  
Author(s):  
Ricardo J.G.B. Campello ◽  
Gérard Favier ◽  
Wagner C. do Amaral
Keyword(s):  
Discrete Time ◽  
Laguerre Functions ◽  
Volterra Models

scholarly journals PARAMETRIC COMPLEXITY REDUCTION OF DISCRETE-TIME LINEAR SYSTEMS HAVING A SLOW INITIAL ONSET OR DELAY

2016 ◽  
Vol 21 (5) ◽  
pp. 668-684 ◽  
Author(s):  
Abdelkader Krifa ◽  
Kais Bouzrara
Keyword(s):  
Discrete Time ◽  
Laguerre Functions ◽  
Orthogonal Functions ◽  
Optimal Value ◽  
Initial Onset ◽  
Kautz Functions ◽  
Time Systems ◽  
Optimal Modeling ◽  
Time Linear ◽  
Recursive Representation

This paper is concerned with an optimal expansion of linear discrete time systems on Meixner functions. Many orthogonal functions have been widely used to reduce the model parameter number such as Laguerre functions, Kautz functions and orthogonal basis functions. However, when the system has a slow initial onset or delay, Meixner functions, which have a slow start, are more suitable in terms of providing a more accurate approximation to the system. The optimal approximation of Meixner model is ensured once the pole characterizing the Meixner functions is set to its optimal value. In this paper, a new recursive representation of Meixner model is proposed. Further we propose, from input/output measurements, an iterative pole optimization algorithm of the Meixner pole functions. The method consists in applying the Newton-Raphson’s technique in which their elements are expressed analytically by using the derivative of the Meixner functions. Simulation results show the effectiveness of the proposed optimal modeling method.

scholarly journals Optimal Expansions of Discrete-Time Volterra Models Using Laguerre Functions

2003 ◽  
Vol 36 (16) ◽  
pp. 1807-1812 ◽  
Author(s):  
Ricardo J.G.B. Campello ◽  
Gérard Favier ◽  
Wagner C. Amaral
Keyword(s):  
Discrete Time ◽  
Laguerre Functions ◽  
Volterra Models

scholarly journals General Six-Step Discrete-Time Zhang Neural Network for Time-Varying Tensor Absolute Value Equations

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Min Sun ◽  
Jing Liu
Keyword(s):  
Neural Network ◽  
Discrete Time ◽  
Numerical Differentiation ◽  
Feasible Region ◽  
Time Varying ◽  
Absolute Value Equations ◽  
Step Size ◽  
Absolute Value ◽  
Free Parameters ◽  
Zhang Neural Network

This article presents a general six-step discrete-time Zhang neural network (ZNN) for time-varying tensor absolute value equations. Firstly, based on the Taylor expansion theory, we derive a general Zhang et al. discretization (ZeaD) formula, i.e., a general Taylor-type 1-step-ahead numerical differentiation rule for the first-order derivative approximation, which contains two free parameters. Based on the bilinear transform and the Routh–Hurwitz stability criterion, the effective domain of the two free parameters is analyzed, which can ensure the convergence of the general ZeaD formula. Secondly, based on the general ZeaD formula, we design a general six-step discrete-time ZNN (DTZNN) for time-varying tensor absolute value equations (TVTAVEs), whose steady-state residual error changes in a higher order manner than those presented in the literature. Meanwhile, the feasible region of its step size, which determines its convergence, is also studied. Finally, experiment results corroborate that the general six-step DTZNN model is quite efficient for TVTAVE solving.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

Sign in / Sign up

Export Citation Format

Share Document