激光相变硬化性能与工艺参数关系的数学模型

用回归正文分析的方法．建立了中碳结构钢、碳素工具钢和合金结构钢的激光相变硬化性能与工艺参数间关系的数学模型。分析了回归力程的显著性并预测了各硬化指标的精度。     

关于布尔函数的二次逼近

本文首先利用矩阵形式引入了二阶ｗａｌｓｈ谱的概念并讨论了其性质，其次引入了二次ｂｅｎｔ函数的概念，同时论述了二次ｂｅｎｔ函数的密码学意义。     

非正态随机变量的可靠性评价方法研究

不确定性及可靠性分析技术已广泛应用到水利及土木系统工程分析中，可靠性计算的核心之一是对随机变量进行正态转换。文中旨在对土木工程系统不确定性及可靠性分析中有着潜在应用前景的非正态随机变量的多项式正态转换方法进行研究。通过在各种条件下非正态随机变量的正态多项式转换成果之评价，发现在水利及土木工程系统可靠性分析中常用的许多分布可以保留其自身的比较好的特征。     

锌钾硼对甜菜产质量效应及最佳施肥措施的研究

本文利用最优回归设计“310”方案,研究了黑土地甜菜 Zn、K_2O、B的效果及合理施用问题。得出甜菜块根产量、含糖、产糖量、灰分 K 和有害 N 与 ZnK_2O、B 肥回归关系显著的回归模型,分析了 Zn、K_2O、B 对上述五个指标的效应,明确了 Zn 是影响各项指标的主要因子,并采用频率筛选法,提出了兼顾上述五项指标的优质高产施肥措施。     

Beyond Wiener–Askey Expansions: Handling Arbitrary PDFs

In this paper we present a Multi-Element generalized Polynomial Chaos (ME-gPC) method to deal with stochastic inputs with arbitrary probability measures. Based on the decomposition of the random space of the stochastic inputs, we construct numerically a set of orthogonal polynomials with respect to a conditional probability density function (PDF) in each element and subsequently implement generalized Polynomial Chaos (gPC) locally. Numerical examples show that ME-gPC exhibits both p- and h-convergence for arbitrary probability measures     

桃林口水库大坝人工监测项目管理软件的开发与应用

介绍了桃林口水库大坝人工监测项目管理软件研制的意义。对软件结构、开发环境、基本计算原理、主要功能进行了详细阐述；并以大坝垂直位移分析为例,详细讲述了回归分析在资料分析中的具体应用。该软件的应用使工程管理水平上了一个新台阶。     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.     

Runout analysis in fatigue investigation

Those individual tests in a series of experiments that are not completed or must be suspended are known as “censored points,” or “runouts”. Inclusion of runouts in data analysis can be problematic, and such ad hoc approaches as ignoring the runout observation or treating it as a failure can significantly affect estimation. The methodology offered here alleviates the handling of runouts and censored data by using the maximum likelihood estimation (MLE) method to incorporate the censored data properly. The methodology is illustrated with an example problem using actual data and the affects of ad hoc approaches are illustrated.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

奇异值分解压制随机噪声的方法及应用

针对随机噪声压制问题，通过对多项式确定的同相轴形状重新选取数据矩阵，再对新选取的数据矩阵进行SVD滤波，从而压制随机噪声。通过对矩阵奇异值分解的研究，揭示了奇异值分解的原理和SVD滤波原理，将一个多道地震记录看作一个图，它可分解为由一系列正交的子图，通过对子图适当选择并结合其它手段，从而达到提高信噪比的目的，为奇异值分解在地球物理中的进一步应用提供了理论依据。实际数据应用表明，该方法去噪手段灵活、保真性好、分辨率高，对一些突然的脉冲干扰、侧反射、以及其他的反向干扰有明显效果。