球面混合插值的逼近性质

球面径向基函数(SBF)和多项式样条函数均为处理球面散乱数据的有效工具. 本文考虑由球面径向基函数与球面多项式函数组成的混合插值模型, 并利用最小二乘法求解该模型. 对于该插值模型, 首先, 给出带Bessel势的Sobolev空间中的Bernstein不等式, 然后利用该不等式建立逼近正定理,并进一步给出该插值工具的误差估计. 最后, 研究该插值方式(即利用最小二乘法求解混合插值模型)的稳定性.     

基于偏最小二乘路径模型的分位效应测度

对于偏最小二乘路径模型的效应分析,为了测度路径模型的分位效应,文章首先给出了偏最小二乘路径模型建模的具体过程.其次,基于潜变量得分与分位回归提出估计平滑分位效应的方法,给出了平滑分位效应的Bootstrap置信带的算法.最后,考虑顾客满意度的分位异质性,对满意度模型的分位效应进行分析.结论表明,该方法是对传统偏最小二乘路径模型的一种补充且可获得更有深度的决策信息.     

两个多重相关变量组的统计分析(3)(偏最小二乘回归与PLS过程)

本文用PLS过程建立多因变量的偏最小二乘回归模型 ,并用具体例子对最小二乘回归(MLR)、主成分回归 (PCK)和偏最小二乘回归 (PLS)进行比较     

基于均匀设计试验利用偏最小二乘回归对边坡稳定性的分析预测

采用均匀设计试验方案对影响边坡稳定性的因素进行测量,既能节约取样开支,又能得到均匀分散且具有代表性的小样本数据.对该小样本数据结合偏最小二乘回归方法,建立了边坡稳定性系数与各影响因素的非线性回归模型.通过对模型结构、变量投影重要性指标、相对残差值及拟合值的分析发现,基于均匀设计试验利用偏最小二乘回归法可用于对边坡稳定性的分析预测.     

偏最小二乘回归分析在均匀设计试验建模分析中的应用

本文分析了目前应用一般的最小二乘法建立均匀试验数据的二次多项式回归模型时存在的局限性,提出了应用偏最小二乘法(Partial least-square,PLS)建立二次多项式回归模型的技术,并且进一步介绍了偏最小二乘回归(PLS回归)在均匀设计中的应用。作者认为,PLS回归分析建模技术将为均匀设计的更广泛应用提供有力的技术支持。     

基于灰色关联分析与PLS-LSSVM的航材备件需求预测模型

航材备件是保障航空装备日常训练和作战正常使用的重要影响因素,针对部分航材备件样本数据量少,影响因素多且复杂多变,预测结果与装备系统完好性要求偏差较大等问题.建立基于灰色关联分析(GRA)与偏最小二乘(PLS)及最小二乘向量机(LSSVM)相结合的航材备件预测模型,采集某无人机航材备件数据,通过对统计数据进行灰色关联分析...     

助推偏最小二乘法（BPLS）及其应用

在生物统计以及数据挖掘中,分类预测是最基本的任务之一。本文将探讨一种新的方法-助推偏最小二乘法（BPLS）。它结合了一系列收缩的偏最小二乘模型,每个模型只有一个主成分。这种新方法和传统的偏最小二乘方法不同,它不需要选择一系列的偏最小二乘成分。只需要确定两个参数即可。通过对真实数据的训练,得以证明这种新方法比传统的偏最小二乘法在防止过度拟合方面有更好的表现,同时能够保证精确度。     

用样条作常微分方程、偏微分方程与积分方程的最小二乘解

本文作为样条方法的一个应用,我们探讨用一维或二维样条来作常微分方程、偏微分方程与积分方程的最小二乘解的方法。首先,我们将这些方程的问题工转换成最小二乘的问题Ⅱ,其次利用基样条(Cardinal spline)的方法把问题Ⅱ归结为解线性方程组的问题Ⅲ。 文中主要结果是定理1—4。前两个是关于常微分方程的,后两个定理分别论述偏微分方程与积分方程。以定理2为例: 问题Ⅰ(ODE): 问题Ⅱ(极值问题): 问题Ⅲ(线性方程组): 其中S_a为基样条,a=*-1,0,…,n,n+1。     

天然气消费量的偏最小二乘支持向量机预测

结合偏最小二乘法和支持向量机的优缺点,提出基于偏最小二乘支持向量机的天然气消费量预测模型。首先,利用偏最小二乘法确定影响天然气消费量的新综合变量,建立以新综合变量为输入,天然气消费量为输出的支持向量机模型,对天然气消费量进行了预测;然后,与多元回归、偏最小二乘回归、普通支持向量机做误差检验比较,验证该方法的可行性与正确性。结果表明,此天然气消费量预测模型具有较高的精确度和应用价值。     

基于最小二乘法的GM(1,1)模型在我国猪肉产量预测中的应用研究

猪肉产量受诸多因素影响,因此数据波动性大,并且具有小样本性及贫信息等特点.本文采用基于最小二乘法的GM(1,1)模型对我国未来几年内猪肉产量进行了短期预测.首先,介绍了GM(1,1)模型;然后,通过最小二乘法的原理弱化波动较大的数据,减少随机性,加强规律性,建立基于最小二乘法的GM(1,1)模型;其次,结合2008至2014年我国猪肉产量数据建立预测模型;最后,使用2014年数据对模型的可靠性进行验证,基于最小二乘法的GM(1,1)模型的预测结果更加接近实际值.预测结果显示未来3年中国猪肉产量将持续增加.该模型为其他相关预测提供了理论依据,也便于我国对未来猪肉产品市场进行宏观调控,维持猪肉市场平衡,避免猪肉价格波动风险.     

Using a Neural Network to Enhance the Decision Making Quality of a Visual Interactive Simulation Model

The use of a neural network to represent the results of a simulation model is described. The neural network is implemented as an interaction within a visual interactive simulation model. All results obtained from the simulation are offered to the neural network. After a suitable period of training the quality of results obtained from the network matches those obtained by running the original simulation model. An example which embeds a neural network as an interaction within a visual interactive simulation model is described. The example shows how the combined system may enhance the decision making quality of a visual interactive simulation model.     

基于ANP-RBF神经网络的化工行业绿色供应商选择

基于绿色供应链理念,提出了化工行业绿色供应商选择的特色指标,构建了化工行业绿色供应商选择的ANP-RBF神经网络模型。通过ANP确定各指标权重,再结合RBF神经网络,从训练数据中提取隐含的知识和规律,能够方便地用于新供应商的选择。该模型求解算法为增量算法,具有很好的可扩展性,从而增加了评价的动态性。算例验证结果表明,将ANP-RBF神经网络模型用于化工行业绿色供应商的选择具有较强的实用性。     

基于PSO_Trainlm BP模型的图像去噪研究

针对在使用BP模型进行图像去噪时,模型存在的对初始权阈值敏感、易陷入局部极小值和收敛速度慢的问题.为了提高模型去噪效率,提出采用改进粒子群神经网络模型进行图像去噪.首先运用改进粒子群算法对BP神经网络权阈值进行初始寻优,再用trainlm BP算法对优化的网络权阈值进一步精确优化,随后建立基于粒子群算法的BP神经网络去噪模型,并将其应用到图像去噪研究中.仿真结果表明,新模型结合了粒子群算法的全局寻优能力和BP算法的局部搜索能力,减小了模型对初始权阈值的敏感性,有效防止了模型陷入局部极小值的可能,提高了图像去噪模型的速度和质量.     

Multicriteria order acceptance decision support in over-demanded job shops: A neural network approach

Order acceptance is an important issue in job shop production systems where demand exceeds capacity. In this paper, a neural network approach is developed for order acceptance decision support in job shops with machine and manpower capacity constraints. First, the order acceptance decision problem is formulated as a sequential multiple criteria decision problem. Then a neural network based preference model for order prioritization is described. The neural network based preference model is trained using preferential data derived from pairwise comparisons of a number of representative orders. An order acceptance decision rule based on the preference model is proposed. Finally, a numerical example is discussed to illustrate the use of the proposed neural network approach. The proposed neural network approach is shown to be a viable method for multicriteria order acceptance decision support in over-demanded job shops.     

Fuzzy neural network modeling for product development

A fairly general product development model is formulated and analyzed based on multiple attribute decision making with emphasis on the treatment of the linguistic and vague aspects by fuzzy logic and up-dating or learning by neural network. Due to the representative ability of fuzzy set theory and the learning or intelligent ability of neural network, the proposed approaches appear to be an effective tool for handling vague and not well-defined systems.     

Intelligent Crack Recognition Based on XFEM and GA-BP Neural Networks北大核心CSCD

基于扩展有限元法(XFEM)和经遗传算法(GA)优化的误差反向传播多层前馈(BP)神经网络(GA-BP)算法,建立了识别结构中裂纹的反演分析模型。模型通过XFEM正向分析获得的测点位移数据训练GA-BP神经网络,并在此基础上利用该网络进行裂纹反向识别。通过两个典型算例对模型的可行性和精度进行了验证,并探讨了网格密度、测点布置、输入数据噪声等对网络识别精度的影响。结果表明,该文的方法可反演线弹性断裂力学重点关注的直线裂纹的几何信息且具有较好的容噪性能,此外,GA-BP神经网络的预测精度较传统BP神经网络普遍更高。     

Asymptotic stability analysis in uncertain multi-delayed state neural networks via Lyapunov–Krasovskii theory

This paper presents a new approach to the analysis of asymptotic stability of artificial neural networks (ANN) with multiple time-varying delays subject to polytope-bounded uncertainties. This approach is based on the Lyapunov–Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique with the use of a recent Leibniz–Newton model based transformation without including any additional dynamics.Three examples with numerical simulations are used to illustrate the effectiveness of the proposed method. The first example considers the neural network with multiple time-varying delays, which may be seen as a particular case of the second example where it is subject to uncertainties and multiple time-varying delays. Finally, the third example analyzes the stability of the neural network with higher numbers of neurons subject to a single time-delay. The Hopf bifurcation theory is used to verify the stability of the system when the origin falls into instability in the bifurcation point.     

A neural network system for solving an assortment problem in the steel industry

A neural network model for solving an assortment problem found in the iron and steel industry is discussed in this paper. The problem arises in the yard where steel plate is cut into rectangular pieces. The neural network model can be categorized as a Hopfield model, but the model is expanded to handle inequality constraints. The idea of a penalty function is used. A large penalty is applied to the network if a constraint is not satisfied. The weights are updated based on the penalty values. A special term is added to the energy function of the network to guarantee the convergence of the neural network which has this feature. The performance of the neural network was evaluated by comparison with an existing expert system. The results showed that the neural network has the potential to identify in a short time near-optimal solutions to the assortment problem. The neural network is used as the core of a system for dealing with the assortment problem. In building the neural networks system for practical use, there were many implementation issues. Some of them are presented here, and the fundamental ideas are explained. The performance of the neural network system is compared to that of the expert system and evaluated from the practical viewpoint. The results show that the neural network system is useful in handling the assortment problem.     

基于卷积神经网络的智能制造过程质量异常诊断

针对现有方法在智能制造过程中诊断能力有限和识别精度不高的问题,提出了一种与智能制造过程相适应的基于卷积神经网络的质量异常诊断模型。首先建立基于实时数据的过程质量图谱,以精准表达制造过程运行状态。其次,构建用于识别质量图谱的卷积神经网络诊断模型。最后,利用滑动窗口取值的方式对当前过程运行状态进行动态诊断,并通过某球磨过程验证了所提方法的有效性与实用性。结果表明,所提方法优于传统浅层模型,能够有效的对过程异常状态进行识别与诊断。     

基于多分辨率分析的股票市场预测模型研究

采用多分辨率分析技术将深证成指收盘数据序列分解为多个子序列,然后采用神经网络技术对每个子序列分别建立预测模型,将各个预测结果叠加后得到最终预测结果.研究首先发现多分辨率技术可以有效提高预测模型的预测精度,表明分析我国股市波动时应该按照不同因素对股市影响大小及周期的差异分别研究,才能更有效分析股市运行状况及对其预测;其次结果表明不同类型神经网络预测模型预测性能差异明显,在选择股市预测模型的神经网络类型时应该注意其学习算法及收敛过程,以便能更好捕获股市变化规律.