great effect of three way catalytic convertor (twc) performance on oxygen sensor output voltage has made the sensor (located after catalyst) as the main signal in almost all today’s twc monitoring algorithms. in this paper output voltage of nonlinear oxygen sensor is estimated using a nonlinear autoregressive with exogenous inputs (narx) model. the estimation uses ecu calculated exhaust gas flo...

In this study, the use of the three-layer feed forward neural network has been investigated for estimating of infinite dilute diffusion coefficient ( D12 ) of supercritical fluid (SCF), liquid and gas binary systems. Infinite dilute diffusion coefficient was spotted as a function of critical temperature, critical pressure, critical volume, normal boiling point, molecular volume in normal boilin...

Initial dissertation research in the area of real-time system identification of ship hydrodynamic coefficients is presented. In accordance with the naval initiative of Seabasing requiring automation of small vessels, a real-time coefficient system identification method is being researched. Ship coefficient system identification has been relatively neglected with efforts limited only to Kalman f...

The influence coefficient method, which has advantages for conducting inverse analysis and sensitivity studies, is extended to simulate cohesive cracking in three-dimensional bodies when the crack path is known in advance. The method is combined with a focal point model to allow simulation of unload–reload cycles as well. This paper summarizes the theory behind the influence coefficient method ...

The coefficient of prediction 2 j P is derived from the PRESS (prediction sum of squares) statistic just as 2 j R is derived from SSE, the error sum of squares. While 2 j R measures quality of fit, 2 j P measures quality of point predictions. Unlike SSE and PRESS, 2 j R and 2 j P are bounded, relative measures ideally suited for statistical modeling. This paper describes the limits, properties,...

In the multivariate regression setting, we propose a flexible varying coefficient model in which the regression coefficients of some predictors are additive functions of other predictors. Marginal integration estimators of the coefficients are developed and their asymptotic properties investigated. Under -mixing, it is found that the estimators of the parameters in the regression coefficients h...

Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the ...

In the present paper we consider the varying coefficient model which represents a useful tool for exploring dynamic patterns in many applications. Existing methods typically provide asymptotic evaluation of precision of estimation procedures under the assumption that the number of observations tends to infinity. In practical applications, however, only a finite number of measurements are availa...