运用人工神经元网络建立氨溶解度计算模型

本文运用RBF－PCR方法建立氨溶液度计算模型，试验结果表明，该模型用于氨溶解度的计算，效果良好。     

Analysis of multiple indicator dilution curves for estimation of renal tubular transport parameters

A model is developed to estimate the renal tubular secretory parameters by the multiple indicator dilution (MID) technique. The model allows the calculation of influx (from plasma space to tubular cell), efflux (from tubular cell to plasma space) and sequestration (from tubular cell to luminal side) rate constants. A program using the MID-statistical analysis with least squares fitting (SALS) system for the estimation of these parameters is presented. A trapezoid rule and a non-linear least squares regression were used to carry out definite integrals of cubic spline function and least squares fitting, respectively. The system presented seems to be useful for the precise and rapid estimation of the tubular secretory rate constants.     

The predictive power of ridge regression: Some quasi-simulation results: Correction to Cattin.

Reports an error in the original article by Philippe Cattin (Journal of Applied Psychology, 1981, Vol. 66, No. 3, pp. 282-290). The fifth sentence in the first paragraph on page 284 contains an error. The sentence should read: "For intermediate values of k?, the ridge regression weights are 'weighted sums' of the OLS regression weights [not models] and of the zero-order sample correlations and tend to decrease in absolute value as k? increases." (The following abstract of this article originally appeared in record 1981-27117-001.) Reviews research indicating that ridge regression tends to improve the mean square error of prediction obtained with ordinary least squares (OLS) regression in a wide range of conditions. MMPI profiles of 861 psychiatric patients and diagnoses from 29 psychologists, as collected by P. E. Meehl (see record 1960-04396-001), were used to examine the gains in cross-validated multiple correlation obtained with ridge regression compared with OLS and equal weights as a function of sample size and ridge constant. A simple formula for estimating the ridge constant was also evaluated. Results that are related to recent developments concerning the use of Bayesian regression procedures show that ridge regression improves both the mean square error of prediction and the cross-validated multiple correlation obtained with OLS when the ratio sample size to number of predictor variables is relatively small. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Impact of infrastructure and local environment on road unsafety. Logistic modeling with spatial autocorrelation

This article aims at modeling the impact of road characteristics and local spatial environment on road (un)safety. The study applies to Belgium where some 1500 people are killed annually on the roads. This statistic corresponds to one of the highest risks in Europe. Road unsafety is expressed here as whether an hectometer of road belongs to a black zone; a black zone is defined as a segment of road where roads accidents are concentrated. Logistic modeling including spatial autocorrelation is used and compared to non-spatial regression. It is shown that a spatial model is needed to avoid biased estimated parameters. Results show that local environment and road infrastructure play a substantial role in the co-occurrence of road accidents. Hence, education and enforcement cannot be the only measures taken to reach a sustainable road safety. To attain their objectives of accident reduction, public authorities should also take their responsibilities in the matter of securing road infrastructure.     

Fuzzy normal regression model and related neural networks

A fuzzy normal regression model that takes normal fuzzy numbers as its elements for fuzzy sampling points is presented in this paper. This is a continuation of the authors work in the paper [1]. The related fuzzy regression NN system makes the model more flexible in order to accommodate the renewing or refining of the sampling process. The essential ideas of fuzzy regression model and the related neural networks will be explained in a clear manner in this work using an application problem.     

软测量模型自动生成系统的研究与开发

设计并开发出一套基于Matlab和VB的软测量建模生成系统。该系统的特点在于能提供多种选择变量和数据处理的方法;集成了多种多元回归神经网络等软测量建模方法及其相互结合的应用,这种结构使得用户能够在各种方法之间比较选择,从而最终和到一个合适的软洲量模型。改变了目前国内外一些软测量软件包只侧重于某种特定的建模方法的状况。同时系统采用了"提前停止"方法,克服一些软测量软件包所建模型的拟合精度高而推广能力不佳的缺点。实际建模应用表明,该系统算法简单、操作灵活,具有良好的人机交互和较高的执行效率。     

Effect of personal characteristics on relationships between attitudes and job performance.

Using 376 airmen, Whitlock and Cureton (see 35: 7252) collected attitude score and biographical data, which is reanalysed in the present study, using multiple regression techniques. "… much of the criterion variance associated with attitude measures is also related to more easily obtained biographical characteristics of workers and biographical variables should be considered when relating attitudes to productivity." (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Matched-pair cohort methods in traffic crash research

Standard analysis of matched-pair cohort data requires information only from pairs in which at least one had the study outcome. This can be useful in traffic fatality studies of characteristics that can vary among vehicle occupants, such as seat belt use, as crash databases often lack information about vehicles in which all survived. However, matching crash victims who were in the same vehicle does not necessarily eliminate confounding by vehicle or crash related factors, because the matched occupants must be in different seat positions. This paper reviews three methods for estimating relative risks in matched-pair crash data. The first, Mantel-Haenszel stratified methods, may produce biased estimates if seat position is associated with the outcome. The second, the double-pair comparison method, was designed to deal with confounding by seat position. If the effects of seat position vary according to some vehicle or crash characteristic which is associated with the study exposure, adjustment for this characteristic may be needed to produce unbiased estimates. Third, conditional Poisson regression and Cox proportional hazards regression can produce unbiased estimates, but may require model interaction terms between seat position and vehicle or crash characteristics. This paper reviews some of the strengths and limitations of each of these methods, and illustrates their use in simulated and real crash data.     

Theoretical and Empirical Analysis of ReliefF and RReliefF

Relief algorithms are general and successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation in regression and classification. In addition, their quality estimates have a natural interpretation. While they have commonly been viewed as feature subset selection methods that are applied in prepossessing step before a model is learned, they have actually been used successfully in a variety of settings, e.g., to select splits or to guide constructive induction in the building phase of decision or regression tree learning, as the attribute weighting method and also in the inductive logic programming.A broad spectrum of successful uses calls for especially careful investigation of various features Relief algorithms have. In this paper we theoretically and empirically investigate and discuss how and why they work, their theoretical and practical properties, their parameters, what kind of dependencies they detect, how do they scale up to large number of examples and features, how to sample data for them, how robust are they regarding the noise, how irrelevant and redundant attributes influence their output and how different metrics influences them.