Sheet forming optimization based on least square support vector regression and intelligent sampling approach

In this paper, a metamodel-based optimization method by integration of support vector regression (SVR) and intelligent sampling strategy is applied to optimize sheet forming design. Compared with other popular metamodeling techniques, the SVR is based on the principle of structure risk minimization (SRM) as opposed to the principle of the empirical risk minimization in conventional regression techniques. Thus, the accuracy and robust metamodel can be obtained. The intelligent sampling strategy is a kind of design of experiment (DOE) essentially. The characteristic of this method is to generate new sample automatically by responses of objective functions. Compared with traditional DOE methods, the number of samples isn’t constant according to different cases. Furthermore, the number of samples and size of design space can be well controlled according to the intelligent strategy. To minimize both objective functions of wrinkling, crack and thickness deformation efficiently, the proposed method is employed as a fast analysis tool to surrogate the time-consuming finite-element (FE) procedure in the iterations of optimization algorithm. An example is studied to illustrate the application of the approach proposed, and it is concluded that the proposed method is feasible for sheet forming optimization.     

移动最小二乘支持向量机

基于移动最小二乘逐点逼近思想,移动权被引入到最小二乘支持向量机的误差变量中,得到新算法的模型.此外,证明了用移动最小二乘支持向量机作函数估计与在特征空间中用移动最小二乘法得到的解是一致的,揭示了移动最小二乘支持向量机所选择的核函数相当于移动最小二乘法所选择基函数组.数值试验与实例进一步验证所提出方法的优越性.     

Adaptive weighted least square support vector machine regression integrated with outlier detection and its application in QSAR

In order to eliminate the influence of unavoidable outliers in training sample on a model's performance, a novel least square support vector machine regression, which combines outlier detection approach and adaptive weight value for the training sample, is proposed and named as adaptive weighted least square support vector machine regression (AWLS-SVM). Firstly, the effective robust 3σ principle is used to detect marked outliers for the training sample. Secondly, based on the training sample without marked outliers, least square support vector machine regression is employed to develop the model and the fitting error of each sample data is obtained. Thirdly, according to the fitting error of each sample data, the initial weight is calculated. The bigger the fitting error of sample data is, the smaller the weight value of the sample data. Thus, the potential outliers, which are not detected by the robust 3σ principle and have bigger fitting errors, have smaller weight values to reduce the influence of the potential outliers on the performance of model. Then, LS-SVM is applied for the weighted sample to develop the model again. Finally, via the proposed weight value iterative method, the weight values of the training sample are converged, and the model with good predicting performance is obtained. To illustrate the performance of AWLS-SVM, simulation experiment is designed to produce the training sample with marked outlier and some non-marked outliers. AWLS-SVM, AWLS-SVM without the robust 3σ principle, LS-SVM with the robust 3σ principle, LS-SVM, and radial basis function network are applied to develop the model based on the designed sample. The results show that the influence of marked and un-marked outliers on the model's performance is eliminated by AWLS-SVM, and that the predicting performance of AWLS-SVM is the best. Furthermore, the AWLS-SVM method was applied to develop the quantitative structure–activity relationships (QSAR) model of HIV-1 protease inhibitors, and the satisfactory result was obtained.     

Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.     

Multi-objective optimization based on meta-modeling by using support vector regression

Practical engineering design problems have a black-box objective function whose forms are not explicitly known in terms of design variables. In those problems, it is very important to make the number of function evaluations as few as possible in finding an optimal solution. So, in this paper, we propose a multi-objective optimization method based on meta-modeling predicting a form of each objective function by using support vector regression. In addition, we discuss a way how to select additional experimental data for sequentially revising a form of objective function. Finally, we illustrate the effectiveness of the proposed method through some numerical examples.     

基于LSSVM的重力坝地震稳定易损性分析

提出了一种基于最小二乘支持向量机的易损性分析方法,将有限元数值计算的结果作为学习样本,建立最小二乘支持向量机模型,进而进行Monte Carlo仿真,获得结构的易损性曲线,在保证计算精度的同时,提高了计算效率。采用该方法对某重力坝挡水坝段进行了动力稳定易损性分析,并对主要参数进行了敏感性分析。结果表明,该坝段能够保证设计地震作用下的动力抗滑稳定性,且有较大的安全裕度。     

基于舍一交叉验证优化最小二乘支持向量机的故障诊断模型

提出一种基于舍一交叉验证优化最小二乘支持向量机(LS-SVM)的旋转机械故障诊断模型。首先将故障信号EMD分解为平稳IMF分量,再选择表征故障调制特征的IMF分量并构造瞬时幅值欧式范数作为故障特征矢量输入到舍一交叉验证(leave-one-outcross-validation, LOO-CV)优化线性核LS-SVM中进行故障识别。EMD分解可自适应分离故障调制信号;瞬时幅值欧式范数矢量的不同表征各类故障的差异;舍一交叉验证优化惩罚因子可以使线性核LS-SVM克服对故障类型与模式编号映射关系先验知识的依赖,提高LS-SVM的故障预测精度和自适应诊断能力。一个深沟球轴承故障诊断实例说明该模型的有效性。     

基于自适应递推LSSVM的硅锰合金成分在线预测

针对硅锰合金埋弧熔炼过程的特点,提出了一种基于自适应递推最小二乘支持向量机(ARLSSVM)的合金成分在线预测模型.该模型以实测工况参数为数据集,当新增一个样本时,分别采用增长记忆递推算法、限定记忆递推算法和缩减记忆递推算法训练最小二乘支持向量机( LSSVM),有效避免高维矩阵的求逆,加快模型更新的速度.然后通过自适...     

最小二乘支持向量机和小波电铲供电机组状态预测

针对电铲供电机组振动时间序列是个非线性、非平稳的复杂时间序列,难以用单一预测方法进行有效预测的问题,建立了一种基于小波分解和最小二乘支持向量机混合模型进行状态预测的方法.首先通过小波分解,将原始振动时间序列分解到不同层次,然后根据分解后各层次分量的特点选择不同的嵌入维数和LS-SVM参数分别进行预测,最后重构得到原始序列的预测值.对某电铲供电机组振动趋势的预测结果表明,该模型的预测性能好于单一的支持向量机预测方法.     

最小二乘支持向量机的扩展及其在时间序列预测中的应用

根据时间序列近期数据较远期数据包含有更多未来信息的思想,对最小二乘支持向量机预测方法进行了扩展,得到了更具一般性的最小二乘支持向量机预测模型,给出了扩展后的预测模型具体算法。两个时间序列的预测实例表明,扩展后的预测方法获得了更好的预测效果,提升了最小二乘支持向量机预测方法的价值。     

Topology optimization for nonlinear dynamic problems: Considerations for automotive crashworthiness

Crashworthiness of automotive structures is most often engineered after an optimal topology has been arrived at using other design considerations. This study is an attempt to incorporate crashworthiness requirements upfront in the topology synthesis process using a mathematically consistent framework. It proposes the use of equivalent linear systems from the nonlinear dynamic simulation in conjunction with a discrete-material topology optimizer. Velocity and acceleration constraints are consistently incorporated in the optimization set-up. Issues specific to crash problems due to the explicit solution methodology employed, nature of the boundary conditions imposed on the structure, etc. are discussed and possible resolutions are proposed. A demonstration of the methodology on two-dimensional problems that address some of the structural requirements and the types of loading typical of frontal and side impact is provided in order to show that this methodology has the potential for topology synthesis incorporating crashworthiness requirements.     

超广角光学镜头畸变的约束最小二乘优化

在普通最小二乘算法的基础上,根据短焦距、超广角光学镜头的实际特性,在线性回归方程组中添加约束条件,并借助 MATLAB Optimization 工具箱求得拟合的结果。计算结果表明, 该方法减小了测量误差对计算结果的影响,改善了畸变随视场的分布,使计算结果更加合理。     

A hybrid finite difference and moving least square method for elasticity problems

In this paper, a novel hybrid finite difference and moving least square (MLS) technique is presented for the two-dimensional elasticity problems. A new approach for an indirect evaluation of second order and higher order derivatives of the MLS shape functions at field points is developed. As derivatives are obtained from a local approximation, the proposed method is computationally economical and efficient. The classical central finite difference formulas are used at domain collocation points with finite difference grids for regular boundaries and boundary conditions are represented using a moving least square approximation. For irregular shape problems, a point collocation method (PCM) is applied at points that are close to irregular boundaries. Neither the connectivity of mesh in the domain/boundary or integrations with fundamental/particular solutions is required in this approach. The application of the hybrid method to two-dimensional elastostatic and elastodynamic problems is presented and comparisons are made with the boundary element method and analytical solutions.     

Algorithm for a class of constrained weight least square problems and applications

An algorithm, based on ordinal optimisation (OO) and sensitive theories, is presented to solve a class of constrained weight least square problems with continuous and discrete variables. the proposed algorithm can cope with an enormous amount of computational complexity problems and has a high probability of obtaining a good enough solution according to the oo theory. this method has some advantages, such as computational efficiency, numerical stability and the superiority of the good enough solution. the proposed algorithm is explicit, compact and easy to program. test results demonstrate that the proposed approach is more computational-efficient than other existing approaches for solving constrained-state estimation problems with continuous and discrete variables on the ieee 30-bus and the ieee 118-bus systems.     

Acoustic emission source localization technique based on least squares support vector machine by using FBG sensors

An intelligent acoustic emission (AE) source localization technique by using fiber Bragg grating (FBG) sensors was investigated. Four FBGs sensing network was established for detecting the AE signal. And power intensity demodulation method was initialized employing narrow-band tunable laser. The intelligent AE source localization method was proposed based on wavelet transform, cross-correlation analysis, and least squares support vector machines (LS-SVM). LS-SVM modal’s input is signal time difference and output is AE source position. The location experiments were carried out on a 500 mm × 500 mm × 2 mm aluminum alloy plate. The results showed that the AE source position abscissa and ordinate localization errors are all less than 10 mm. The maximum and average localization errors are 8.65 and 6.78 mm, respectively. The research results provided a novel method for AE source localization by using FBG sensors.     

一种光滑支持向量回归机新函数的研究

为支持向量回归机提供了一个新的光滑函数,即运用三次样条函数和复合函数的方法,得到一种新的光滑支持向量回归机——三次样条光滑支持向量回归机(TSSSVR).对该支持向量回归机的光滑函数进行了逼近性能和收敛性的分析,并说明该光滑函数比以往的光滑函数具有更高的逼近精度和收敛速度.     

An adaptive response surface method for crashworthiness optimization

Response surface-based design optimization has been commonly used for optimizing large-scale design problems in the automotive industry. However, most response surface models are built by a limited number of design points without considering data uncertainty. In addition, the selection of a response surface in the literature is often arbitrary. This article uses a Bayesian metric to systematically select the best available response surface among several candidates in a library while considering data uncertainty. An adaptive, efficient response surface strategy, which minimizes the number of computationally intensive simulations, was developed for design optimization of large-scale complex problems. This methodology was demonstrated by a crashworthiness optimization example.     

基于PSO优选参数的SVR水质参数遥感反演模型

为进一步提高多光谱图像水质反演的精度,提出了一种基于PSO优选参数的SVR水质参数遥感反演模型。该模型利用高分辨率多光谱遥感SPOT-5数据和水质实地监测数据,采用CV估计模型推广误差,并使用PSO优选SVR模型参数,实现了模型参数的自动全局优选,在训练好的SVR模型基础之上对水质进行反演。以渭河陕西段为例进行实证研究,实验结果表明,所提出的水质反演模型较常规的线性回归模型有更高的反演精度,为内陆河流环境遥感监测提供了一种新方法。     

A computationally efficient metamodeling approach for expensive multiobjective optimization

This paper explores a new metamodeling framework that may collapse the computational explosion that characterizes the modeling of complex systems under a multiobjective and/or multidisciplinary setting. Under the new framework, a pseudo response surface is constructed for each design objective for each discipline. This pseudo response surface has the unique property of being highly accurate in Pareto optimal regions, while it is intentionally allowed to be inaccurate in other regions. In short, the response surface for each design objective is accurate only where it matters. Because the pseudo response surface is allowed to be inaccurate in other regions of the design space, the computational cost of constructing it is dramatically reduced. An important distinguishing feature of the new framework is that the response surfaces for all the design objectives are constructed simultaneously in a mutually dependent fashion, in a way that identifies Pareto regions for the multiobjective problem. The new framework supports the puzzling notion that it is possible to obtain more accuracy and radically more design space exploration capability, while actually reducing the computation effort. This counterintuitive metamodeling paradigm shift holds the potential for identifying highly competitive products and systems that are well beyond today’s state of the art.