回归设计在作物间作技术研究中的应用

本研究采用回归正交组合设计的方法,选择春玉米间作春大豆的畦幅、春玉米密度、春大豆密度三个农艺因子,布置田间参数试验,通过对试验结果的分析,建立了春玉米间作春大豆的总产、春玉米产量、春大豆产量及净产值的三元二次回归模型。根据建立的模型,进行了因子效应分析,并结合计算机模拟寻优,对春玉米间作春大豆的畦幅及其复合群体结构进行了优化。     

回归旋转设计在农艺优化试验中的应用

1984年台州地区农技部门选取了汕优六号农艺系统中的秧令:x_1天、插秧密度:x_2万丛/亩、尿素:x_3斤/亩、氯化钾:x_4斤/亩和猪栏:x_5斤/亩等五个关键农艺因子,采用五元二次回归正交旋转设计方法,进行五因子、五水平、多指标的多点试验,建立了“汕优六号亩产超千斤的最优农艺数学模型”。试验分别在A,B,C三试点同时进行.各试点均安排36个小区,其中五因子二水平全因子试验的二分之一实施16个小区,星号点和零水平中心点试验小区各10个. 以三试点各小区亩产为指标,算得总回归模型为     

玉米高产栽培综合农艺措施教学模型的研究

本文根据农业系统工程的原理，应用二次回归正交旋转组合设计的方法，将对玉米生育影响较大的主要裁培因素做为决策变量以最大的产量指标做为目标函数，通过田间试验和生产实施，对影响玉米生产的各个要素进行了模型化研究，应用微机进行了参数测辨和统计分析，建立了产量函数模型，解析了各因子的产量效应和交互作用。通过模拟寻优，优选出了本市玉米生产最优的综合农艺措施方案。     

玉米高产栽培综合农艺措施数学模型的研究

本文根据农业系统工程的原理，应用二次回归正交旋转组合设计的方法，将对玉米生育影响较大的主要栽培因素做为决策变量以最大的产量指标做为目标函数，通过田间试验和生产实施，对影响玉米生产的各个要素进行了模型化研究，应用微机进行了参数测辨和统计分析，建立了产量函数模型，解析了各因子的产量效应和交互作用。通过模拟寻优，优选出了本市玉米生产最优的综合农艺措施方案。     

隐变量交互效应分析方法的比较与评价

本文通过一个实例,比较了分析隐变量交互效应的主要方法,包括用隐变量的因子得分做回归分析、分组线性结构方程模型分析、加入乘积项的结构方程模型分析和两步最小二乘回归分析,并评价了这些方法的优缺点。     

Q和Q_B准则在Double设计中的应用

一阶回归模型通常用来从众多因子当中筛选出效应显著的因子,而Q和QB准则能够比较简单地从众多合格的拟合子模型中找出最优设计.因此本文主要探讨了极大模型为一阶回归模型时,初始设计d分别与Double设计d dd-d,完全反转设计d-d,设计dd11-dd22之间的关系,以及它们在Q和QB准则下的最优性关系.     

回归设计在增产菌、稀土、叶面宝混配试验中的应用

在农业科学实验中,经常会遇到确定最优农艺和最佳配方及制定自动控制中的数学模型等问题,在不完全了解生产过程中物理、化学和生物原理的情况下,用回归设计与分析的方法来解决是比较有效的.其优点是;试验处理组合少,计算方便,获得的信息多,其中一次回归的正交设计常被用来确定最优生产条件和因子筛选,二次回归的正交设计主要用于寻求最优配方和建立生产过程中的数学模型.本文介绍的是这两种设计方案在增产菌、稀土和叶面宝三     

两套种作物的农艺优化教学模型研究

本文是采用回归旋转设计方法，研究两套种作物农艺优化的数学模型．利用计算机模拟仿真，优化农艺方案．示范验证表明，这种试验方法可行，优化方案应用效果显著．     

2(n1+n2)-(k1+k2)部分因析裂区设计中纯净两因子交互效应的最大数目的界

部分因析裂区(FFSP)设计因其特殊结构而具有重要的研究价值.一个FFSP设计中有两类因子: 全区(WP)因子和子区(SP)因子,它们可以组成3种两因子交互效应: WP两因子交互效应, WS两因子效应和SP两因子交互效应.本文在纯净效应准则下考虑分辨度III和IV的FFSP设计, 得到了FFSP设计中纯净WP两因子交互效应及WS两因子交互效应的最大数目的上、下界,给出了该数目达到下界的FFSP设计的构造方法,并进一步考察了这些构造方法的实际效果.     

农艺措施对蔗糖产量影响的探讨

采用五元二次回归旋转正交组合设计方法，通过１９９０—１９９１年秋植蔗试验，建立蔗糖产量与种植密度，尿素、钙镁磷、氯化钾、桐麸的施用量等主要农艺措施之间的数学模型，得出各主要农艺措施及它们相互之间的作用对蔗糖产量的影响程度，并优选甘蔗生产的最优综合栽培农艺措施方案。     

Optimizing stochastic production-inventory systems: A heuristic based on simulation and regression analysis

We present a heuristic optimization method for stochastic production-inventory systems that defy analytical modelling and optimization. The proposed heuristic takes advantage of simulation while at the same time minimizes the impact of the dimensionality curse by using regression analysis. The heuristic was developed and tested for an oil and gas company, which decided to adopt the heuristic as the optimization method for a supply-chain design project. To explore the performance of the heuristic in general settings, we conducted a simulation experiment on 900 test problems. We found that the average cost error of using the proposed heuristic was reasonably low for practical applications.     

Advances in concrete arch dams shape optimization

This paper presents an efficient methodology to find the optimum shape of arch dams. In order to create the geometry of arch dams a new algorithm based on Hermit Splines is proposed. A finite element based shape sensitivity analysis for design-dependent loadings involving body force, hydrostatic pressure and earthquake loadings is implemented. The sensitivity analysis is performed using the concept of mesh design velocity. In order to consider the practical requirements in the optimization model such as construction stages, many geometrical and behavioral constrains are included in the model in comparison with previous researches. The optimization problem is solved via the sequential quadratic programming (SQP) method. The proposed methods are applied successfully to an Iranian arch dam, and good results are achieved. By using such methodology, efficient software for shape optimization of concrete arch dams for practical and reliable design now is available.     

试验设计中多元回归分析方法的研究

在一试验设计的实例分析研究的基础上 ,提出了多元回归分析在试验设计中的应用方法。通过对试验数据建立的回归模型 ,可以预测到更好的因素水平 ,对第二轮试验设计具有重要的指导作用     

Infinitesimal perturbation analysis for second derivative estimation and design of manufacturing flow controllers

A simulation-based numerical technique for the design of near-optimal manufacturing flow controllers for unreliable flexible manufacturing systems uses quadratic approximations of the value functions that characterize the optimal policy and employs stochastic optimization to design the key coefficients of the quadratic approximations. First and second derivative estimates that drive the optimization algorithm are obtained from a single sample path of the system via infinitesimal perturbation analysis (IPA). Extensive computational experience is reported for one, two, and three-part-type production systems. The relative performance of first-order and second-order stochastic optimization algorithms is investigated. The computational efficiency of these algorithms is finally compared to conventional controller design algorithms based on state-space discretization and successive approximation.This research was supported by the National Science Foundation, Grant No. DDM-89-14277 and DDM-9215368.     

Spatial Regression Models,Response Surfaces,and Process Optimization

Abstract

Spatial regression models are developed as a complementary alternative to second-order polynomial response surfaces in the context of process optimization. These models provide estimates of design variable effects and smooth, data-faithful approximations to the unknown response function over the design space. The predicted response surfaces are driven by the covariance structures of the models. Several structures, isotropic and anisotropic, are considered and connections with thin plate splines are reviewed. Estimation of covariance parameters is achieved via maximum likelihood and residual maximum likelihood. A feature of the spatial regression approach is the visually appealing graphical summaries that are produced. These allow rapid and intuitive identification of process windows on the design space for which the response achieves target performance. Relevant design issues are briefly discussed and spatial designs, such as the packing designs available in Gosset, are suggested as a suitable design complement. The spatial regression models also perform well with no global design, for example with data obtained from series of designs on the same space of design variables. The approach is illustrated with an example involving the optimization of components in a DNA amplification assay. A Monte Carlo comparison of the spatial models with both thin plate splines and second-order polynomial response surfaces for a scenario motivated by the example is also given. This shows superior performance of the spatial models to the second-order polynomials with respect to both prediction over the complete design space and for cross-validation prediction error in the region of the optimum. An anisotropic spatial regression model performs best for a high noise case and both this model and the thin plate spline for a low noise case. Spatial regression is recommended for construction of response surfaces in all process optimization applications.     

Efficient topology optimization of thermo-elasticity problems using coupled field adjoint sensitivity analysis method

We develop a unified and efficient adjoint design sensitivity analysis (DSA) method for weakly coupled thermo-elasticity problems. Design sensitivity expressions with respect to thermal conductivity and Young's modulus are derived. Besides the temperature and displacement adjoint equations, a coupled field adjoint equation is defined regarding the obtained adjoint displacement field as the adjoint load in the temperature field. Thus, the computing cost is significantly reduced compared to other sensitivity analysis methods. The developed DSA method is further extended to a topology design optimization method. For the topology design optimization, the design variables are parameterized using a bulk material density function. Numerical examples show that the DSA method developed is extremely efficient and the optimal topology varies significantly depending on the ratio of mechanical and thermal loadings.     

Perspective functions: Proximal calculus and applications in high-dimensional statistics

Perspective functions arise explicitly or implicitly in various forms in applied mathematics and in statistical data analysis. To date, no systematic strategy is available to solve the associated, typically nonsmooth, optimization problems. In this paper, we fill this gap by showing that proximal methods provide an efficient framework to model and solve problems involving perspective functions. We study the construction of the proximity operator of a perspective function under general assumptions and present important instances in which the proximity operator can be computed explicitly or via straightforward numerical operations. These results constitute central building blocks in the design of proximal optimization algorithms. We showcase the versatility of the framework by designing novel proximal algorithms for state-of-the-art regression and variable selection schemes in high-dimensional statistics.     

Better Optimization of Nonlinear Uncertain Systems (BONUS) for Vehicle Structural Design

With the continuous improvement of computational performance, vehicle structural design has been addressed using computational methods, resulting in more efficient development of new vehicles. Most simulation-based optimization approaches generate deterministic optimal designs without considering variability effects in modeling, simulation, and/or manufacturing. One of the main reasons for this omission is due to the fact that the computing time of a single crash analysis for vehicle structural design still requires significant computing time using a state-of-the-art computer. This calls for the development and implementation of an efficient optimization under uncertainty method. In this paper, a new integrated stochastic optimization method, which combines the advantages of metamodeling techniques and Better Optimization of Nonlinear Uncertain Systems (BONUS), is developed for vehicle side impact design. Nonlinear metamodels are built by using a stepwise regression method to replace the expensive computational model and BONUS is employed to obtain optimal designs under uncertainty. A benchmark problem for vehicle safety design is used to demonstrate the method. The main goal of this case study is to maintain or enhance the vehicle side impact test performance while minimizing the vehicle weight under various uncertainties.     

基于支持向量机的粒子群神经网络集成股市预测模型

为有效提高神经网络集成的泛化能力,先利用量子粒子群和主成分分析提高集成个体的泛化能力,再利用泛化能力强的支持向量机回归集成生成输出结论,建立一个基于支持向量机的粒子群神经网络集成股市预测模型.试验表明,该模型能有效提高神经网络集成系统的泛化能力,预测精度高,稳定性好.     

空气中PM2.5问题的研究

主要研究空气污染中的PM2.5扩散问题.首先利用相关分析法讨论了PM2.5与SO_2,NO_2,CO,PM10,O_3的相关性,建立线性回归方程;然后建立一维的反应扩散方程,预测PM2.5浓度变化,并定量与定性分析西安市空气污染状况;再建立高斯烟羽模型,对持续高浓度PM2.5扩散情形进行拟合,并对污染物扩散范围进行预测,得到重度污染以及可能安全区域;最后通过建立最优化模型,得到较有经济效益的空气治理方案.