System identifications by SIRMs models with linear transformation of input variables
نویسندگان
چکیده
منابع مشابه
Selecting Input Variables for Fuzzy Models
We present an efficient method for selecting important input variables when building a fuzzy model from data. Past methods for input variable selection require generating different models while searching for the optimal combination of variables; our method requires generating only one model that employs all possible input variables. To determine the important variables, premises in the fuzzy ru...
متن کاملGraph Transformation with Variables
Variables make rule-based systems more abstract and expressive, as witnessed by term rewriting systems and two-level grammars. In this paper we show that variables can be used to define advanced ways of graph transformation as well. Taking the gluing approach to graph transformation [7, 3] as a basis, we consider extensions of rules with attribute variables, clone variables, and graph variables...
متن کاملSemiparametric analysis of linear transformation models with covariate measurement errors.
We take a semiparametric approach in fitting a linear transformation model to a right censored data when predictive variables are subject to measurement errors. We construct consistent estimating equations when repeated measurements of a surrogate of the unobserved true predictor are available. The proposed approach applies under minimal assumptions on the distributions of the true covariate or...
متن کاملOn Estimation of Partially Linear Transformation Models.
We study a general class of partially linear transformation models, which extend linear transformation models by incorporating nonlinear covariate effects in survival data analysis. A new martingale-based estimating equation approach, consisting of both global and kernel-weighted local estimation equations, is developed for estimating the parametric and nonparametric covariate effects in a unif...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Artificial Intelligence Research
سال: 2016
ISSN: 1927-6982,1927-6974
DOI: 10.5430/air.v5n2p55