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Model Form (model + form)

Distribution by Scientific Domains

Engineering	62%

Selected Abstracts

APPROXIMATION PROPERTIES OF TP MODEL FORMS AND ITS CONSEQUENCES TO TPDC DESIGN FRAMEWORK

ASIAN JOURNAL OF CONTROL, Issue 3 2007
Domonkos Tikk
ABSTRACT Tensor Product Distributed Compensation (TPDC) is a recently established controller design framework, that links TP model transformation and Parallel Distributed Compensation (PDC) framework. TP model transformation converts different models to a common representational form: the TP model form. The primary aim of this paper is to investigate the approximation capabilities of TP model forms, because the universal applicability of TPDC framework strongly relies on it. We point out that the set of functions that can be approximated arbitrarily well by TP forms with bounded number of components lies no-where dense in the set of continuous functions. Consequently, in a class of control problems this property necessitates the usage of tradeoff techniques between the accuracy and the complexity of the TP form, which is easily feasible within the TPDC framework unlike in analytic models. [source]

A Probabilistic Framework for Bayesian Adaptive Forecasting of Project Progress

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2007
Paolo Gardoni
An adaptive Bayesian updating method is used to assess the unknown model parameters based on recorded data and pertinent prior information. Recorded data can include equality, upper bound, and lower bound data. The proposed approach properly accounts for all the prevailing uncertainties, including model errors arising from an inaccurate model form or missing variables, measurement errors, statistical uncertainty, and volitional uncertainty. As an illustration of the proposed approach, the project progress and final time-to-completion of an example project are forecasted. For this illustration construction of civilian nuclear power plants in the United States is considered. This application considers two cases (1) no information is available prior to observing the actual progress data of a specified plant and (2) the construction progress of eight other nuclear power plants is available. The example shows that an informative prior is important to make accurate predictions when only a few records are available. This is also the time when forecasts are most valuable to the project manager. Having or not having prior information does not have any practical effect on the forecast when progress on a significant portion of the project has been recorded. [source]

Error estimation of closed-form solution for annual rate of structural collapse

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 15 2008
Brendon A. Bradley
Abstract With the increasing emphasis of performance-based earthquake engineering in the engineering community, several investigations have been presented outlining simplified approaches suitable for performance-based seismic design (PBSD). Central to most of these PBSD approaches is the use of closed-form analytical solutions to the probabilistic integral equations representing the rate of exceedance of key performance measures. Situations where such closed-form solutions are not appropriate primarily relate to the problem of extrapolation outside of the region in which parameters of the closed-form solution are fit. This study presents a critical review of the closed-form solution for the annual rate of structural collapse. The closed-form solution requires the assumptions of lognormality of the collapse fragility and power model form of the ground motion hazard, of which the latter is more significant regarding the error of the closed-form solution. Via a parametric study, the key variables contributing to the error between the closed-form solution and solution via numerical integration are illustrated. As these key variables cannot be easily measured, it casts doubt on the use of such closed-form solutions in future PBSD, especially considering the simple and efficient nature of using direct numerical integration to obtain the solution. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Gender differences in science attitude-achievement relationships over time among white middle-school students

JOURNAL OF RESEARCH IN SCIENCE TEACHING, Issue 4 2002
Nancy Mattern
Four causal models describing the longitudinal relationships between attitudes and achievement have been proposed in the literature. These models feature: (a) cross-effects over time between attitudes and achievement, (b) influence of achievement predominant over time, (c) influence of attitudes predominant over time, or (d) no cross-effects over time between attitudes and achievement. In an examin-ation of the causal relationships over time between attitudes toward science and science achievement for White rural seventh- and eighth-grade students, the cross-effects model was the best fitting model form for students overall. However, when examined by gender, the no cross-effects model exhibited the most accurate fit for White rural middle-school girls, whereas a new model called the no attitudes-path model exhibited the best fit for these boys. © 2002 Wiley Periodicals Inc. J Res Sci Teach 39: 324,340, 2002 [source]

Non-parametric habitat models with automatic interactions

JOURNAL OF VEGETATION SCIENCE, Issue 6 2006
Bruce McCune
Abstract Questions: Can a statistical model be designed to represent more directly the nature of organismal response to multiple interacting factors? Can multiplicative kernel smoothers be used for this purpose? What advantages does this approach have over more traditional habitat modelling methods? Methods: Non-parametric multiplicative regression (NPMR) was developed from the premises that: the response variable has a minimum of zero and a physiologically-determined maximum, species respond simultaneously to multiple ecological factors, the response to any one factor is conditioned by the values of other factors, and that if any of the factors is intolerable then the response is zero. Key features of NPMR are interactive effects of predictors, no need to specify an overall model form in advance, and built-in controls on overfitting. The effectiveness of the method is demonstrated with simulated and real data sets. Results: Empirical and theoretical relationships of species response to multiple interacting predictors can be represented effectively by multiplicative kernel smoothers. NPMR allows us to abandon simplistic assumptions about overall model form, while embracing the ecological truism that habitat factors interact. [source]

APPROXIMATION PROPERTIES OF TP MODEL FORMS AND ITS CONSEQUENCES TO TPDC DESIGN FRAMEWORK

A general model for predicting brown tree snake capture rates

ENVIRONMETRICS, Issue 3 2003
Richard M. Engeman
Abstract The inadvertent introduction of the brown tree snake (Boiga irregularis) to Guam has resulted in the extirpation of most of the island's native terrestrial vertebrates, has presented a health hazard to small children, and also has produced economic problems. Trapping around ports and other cargo staging areas is central to a program designed to deter dispersal of the species. Sequential trapping of smaller plots is also being used to clear larger areas of snakes in preparation for endangered species reintroductions. Traps and trapping personnel are limited resources, which places a premium on the ability to plan the deployment of trapping efforts. In a series of previous trapping studies, data on brown tree snake removal from forested plots was found to be well modeled by exponential decay functions. For the present article, we considered a variety of model forms and estimation procedures, and used capture data from individual plots as random subjects to produce a general random coefficients model for making predictions of brown tree snake capture rates. The best model was an exponential decay with positive asymptote produced using nonlinear mixed model estimation where variability among plots was introduced through the scale and asymptote parameters. Practical predictive abilities were used in model evaluation so that a manager could project capture rates in a plot after a period of time, or project the amount of time required for trapping to reduce capture rates to a desired level. The model should provide managers with a tool for optimizing the allocation of limited trapping resources. Copyright © 2003 John Wiley & Sons, Ltd. [source]