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.     

Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity

Support vector machine (SVM), which is a new technology solving classification and regression, has been widely used in many fields. In this study, based on the integrated conductivity(including conductivity and tensile strength) data obtained by carbon fiber/ABS resin matrix composites experiment, a predicting and optimizing model using genetic algorithm-least squares support vector regression (GA-LSSVR) was developed. In this model, genetic algorithm (GA) was used to select and optimize parameters. The predicting results agreed with the experimental data well. By comparing with principal component analysis-genetic back propagation neural network (PCA-GABPNN) predicting model, it is found that GA-LSSVR model has demonstrated superior prediction and generalization performance in view of small sample size problem. Finally, an optimized district of performance parameters was obtained and verified by experiments. It concludes that GA-LSSVR modeling method provides a new promising theoretical method for material design.     

Failure and reliability prediction by support vector machines regression of time series data

Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques.     

基于支持向量机的非概率可靠性分析

针对具有隐式极限状态函数的不确定性结构的非概率可靠性分析,提出一种基于支持向量机回归的不确定性结构非概率可靠性分析方法,并给出了该方法的分析流程.抽取数据样本,优化支持向量机回归参数,使用训练后的支持向量机替代隐式极限状态函数.利用非概率集合理论中的区间分析法,引入尺寸比例因子,构造合适的不确定性结构非概率可靠性指标迭...     

Quantitative analysis of carbon content in fly ash using LIBS based on support vector regression

Laser-induced breakdown spectroscopy (LIBS) has been proved as an on-line detection technology to measure the carbon content in fly ash, which is beneficial for immediate assessment of the boiler combustion efficiency. Support vector regression (SVR) was adopted as the quantitative model for the carbon content measurement in fly ash in this study. Ash species was one of the key factors affecting quantitative accuracy. Experiments have proven that, the index of plasma temperature and the electron density among different species could be similar, while the partition function ratios and the temperature correction factor showed obvious differences among different ash species. Based on the partition function ratios, the Matrix Effect Correction Factor (MECF) was defined. SVR model was optimized by MECF and the analysis results showed that the correlation coefficient of calibration (R²) increased from 0.989 to 0.991, the root-mean-square error of calibration (RMSEC) decreased from 2.02% to 0.850%, the root-mean-square error of prediction (RMSEP) decreased from 2.13% to 1.07%, and averaged relative standard deviation (ARSD) decreased from 8.62% to 1.89%. The results showed that SVR combined with MECF was an effective method to improve the accuracy of LIBS quantitative analysis of the carbon content in fly ash.     

基于多输出支持向量回归机的有限元模型修正

为了克服神经网络以及单输出支持向量回归算法在有限元模型修正中的不足,提出了基于多输出支持向量回归算法的有限元模型修正方法。根据5-折交叉验证法选择支持向量回归机的参数,用均匀试验设计法构造样本,联合结构的动力和静力响应数据作为输入,多个设计参数作为输出,以支持向量回归机逼近输入输出二者之间的非线性映射关系,然后利用支持向量回归机的泛化推广能力,求解设计参数的目标值。空间网格结构数值模型的分析结果表明,该方法能同时修正多个设计参数,在少量样本的情况下具有较高的修正精度,为有限元模型修正提供了一种新的探索。     

A model for preventive maintenance planning by genetic algorithms based in cost and reliability

This work has two important goals. The first one is to present a novel methodology for preventive maintenance policy evaluation based upon a cost-reliability model, which allows the use of flexible intervals between maintenance interventions. Such innovative features represents an advantage over the traditional methodologies as it allows a continuous fitting of the schedules in order to better deal with the components failure rates. The second goal is to automatically optimize the preventive maintenance policies, considering the proposed methodology for systems evaluation.Due to the great amount of parameters to be analyzed and their strong and non-linear interdependencies, the search for the optimum combination of these parameters is a very hard task when dealing with optimizations schedules. For these reasons, genetic algorithms (GA) may be an appropriate optimization technique to be used. The GA will search for the optimum maintenance policy considering several relevant features such as: (i) the probability of needing a repair (corrective maintenance), (ii) the cost of such repair, (iii) typical outage times, (iv) preventive maintenance costs, (v) the impact of the maintenance in the systems reliability as a whole, (vi) probability of imperfect maintenance, etc. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR was used as a case study. The results obtained by this methodology outline its good performance, allowing specific analysis on the weighting factors of the objective function.     

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。     

Modeling thermal conductivity enhancement of metal and metallic oxide nanofluids using support vector regression

Enhancing thermal conductivity of nanofluids is an important objective in heat transfer applications. Experimental measurement of thermal conductivity is time consuming, laborious and expensive. One of the common ways to address these limitations involves developing theoretical models to study thermo-physical properties of nanofluid. However, most classical and empirical models fail in predicting experimental results with good precision. In this study, we developed support vector regression (SVR) models that are capable of predicting the thermal conductivity enhancement for metallic and metallic-oxide nanofluids. The accuracy and reliability of the developed models were assessed using statistical parameters such as correlation coefficient (R²), root mean square error (RMSE) and mean absolute error (MAE). The models were characterized with very high correlation coefficients of 99.3 and 96.3% for the metallic and metallic oxide nanofluids, respectively. While the RMSE obtained were 1.11 and 1.33 for the metallic and metallic oxide nanofluids, respectively. In addition, the results of the models were compared with Hamilton-Crosser (HC) model and other empirical models. The SVR models performed much better than all the models examined. Furthermore, the effects of temperature, volume fractions, nanoparticle size and type, and basefluids types were correlated with experimental data in order to assess the performance of the developed models. The results indicate that SVR predictions were accurate and better than common theoretical models.     

Fast Monte Carlo reliability evaluation using support vector machine

This paper deals with the feasibility of using support vector machine (SVM) to build empirical models for use in reliability evaluation. The approach takes advantage of the speed of SVM in the numerous model calculations typically required to perform a Monte Carlo reliability evaluation. The main idea is to develop an estimation algorithm, by training a model on a restricted data set, and replace system performance evaluation by a simpler calculation, which provides reasonably accurate model outputs. The proposed approach is illustrated by several examples. Excellent system reliability results are obtained by training a SVM with a small amount of information.     

车内噪声声品质的支持向量机预测

对多元线性回归、神经网络和支持向量机的三个预测模型进行了研究。以车内噪声为例,建立了基于以上三种方法的车内噪声声品质预测模型,并采用留一法交叉检验作比较,所构建的支持向量机模型预测精度高于其他两种方法。实验结果同时也表明,支持向量计算法具有较强的稳健性和良好的泛化能力,能够用于车内噪声声品质的预测。     

Availability allocation to repairable systems with genetic algorithms: a multi-objective formulation

This paper describes a methodology based on genetic algorithms (GA) and experiments plan to optimize the availability and the cost of reparable parallel-series systems. It is a NP-hard problem of multi-objective combinatorial optimization, modeled with continuous and discrete variables. By using the weighting technique, the problem is transformed into a single-objective optimization problem whose constraints are then relaxed by the exterior penalty technique. We then propose a search of solution through GA, whose parameters are adjusted using experiments plan technique. A numerical example is used to assess the method.     

基于遗传支持向量机的柴油机辐射噪声品质预测技术

以一台六缸车用柴油机为例,研究了其在变负荷及转速工况下表面辐射噪声品质情况,为进一步提高整机声品质,开展柴油机结构声学设计奠定了理论基础。研究国内外车用柴油机客观评价特征,并选取响度、尖锐度、粗糙度和波动度来描述辐射噪声的客观评价特征;针对柴油机噪声特点,采用成对比较法开展以专业陪审团人群为目标的满意度评价研究;应用遗传算法优化支持向量机（GA-SVM）建立起该车用柴油机声品质预测模型,并与BP神经网络预测模型进行比较,结果表明,基于遗传算法优化的支持向量机辐射噪声品质预测模型较神经网络建模预测精度更高,能够更准确地反映客观评价参量与主观满意度之间的非线性映射关系。     

基于支持向量回归外推的统计最优近场声全息研究

通过数值仿真方法,研究统计最优近场声全息中全息面孔径大小对重建精度影响。结果表明当全息面孔径大于重建面孔径2个采样间隔便能获得较高的重建精度,再继续增大全息面孔径也可以提高重建精度,但是趋势变缓。在此基础上,进一步提出了一种利用支持向量回归对全息面孔径进行外推的方法,在不增加测量孔径的前提下,可以通过数据外推增大全息面孔径,提高重建精度。对方形简支钢板辐射声场的仿真结果,验证了方法的有效性。     

Multiobjective optimization by genetic algorithms: application to safety systems

When attempting to optimize the design of engineered systems, the analyst is frequently faced with the demand of achieving several targets (e.g. low costs, high revenues, high reliability, low accident risks), some of which may very well be in conflict. At the same time, several requirements (e.g. maximum allowable weight, volume etc.) should also be satisfied. This kind of problem is usually tackled by focusing the optimization on a single objective which may be a weighed combination of some of the targets of the design problem and imposing some constraints to satisfy the other targets and requirements. This approach, however, introduces a strong arbitrariness in the definition of the weights and constraints levels and a criticizable homogenization of physically different targets, usually all translated in monetary terms.The purpose of this paper is to present an approach to optimization in which every target is considered as a separate objective to be optimized. For an efficient search through the solution space we use a multiobjective genetic algorithm which allows us to identify a set of Pareto optimal solutions providing the decision maker with the complete spectrum of optimal solutions with respect to the various targets. Based on this information, the decision maker can select the best compromise among these objectives, without a priori introducing arbitrary weights.     

支持向量像素抽样的快速图像匹配方法

提出了一种新的支持向量(SupportVectorMachines,SVM)回归的快速图像匹配方法。该方法将匹配模板图像中每个像素的位置坐标和灰度信息作为训练样本,通过选择合适的模型参数,进行SVM回归训练,获得少量的支持向量。依据SVM位置坐标对模板图像进行像素抽样,实现匹配数据的有效压缩。定义了图像支持特征向量,用少量的特征数据描述整幅图像变化的结构信息,保证了匹配数据的置信度。采用相关系数作为相似性测度,实现互相关匹配。实验结果显示,在一幅100×100的光学图像中提取85个支持特征向量点作为匹配数据,匹配概率可达到100%,匹配速度比传统相关匹配方法快近四倍。     

Modelling and analysis of waviness reduction in soft-pad grinding of wire-sawn silicon wafers by support vector regression

The manufacturing of silicon wafers forms the most important step in the construction of integrated circuit (IC) chips. One of the difficulties in this manufacture process is the removal of the waviness from the resulting wafers. In this paper, mathematical modelling and analysis of this removal process is carried out by the use of the support vector regression (SVR) algorithm. The results show that SVR is ideally suited for the modelling of this complicated process. Furthermore, by the use of the learning ability of SVR, the model can be continuously improved as more data become available. Based on the resulting model, the influences of the various factors on the rate of removal and the ease of control of the removal process are also discussed.     

红外序列图像的支持向量机分割方法

红外序列图像的准确分割是自动目标识别的关键,而当图像背景复杂时,传统的图像分割技术往往难以满足要求,为此,提出了基于支持向量机的红外序列图像分割方法。序列图像中的部分帧被作为训练样本,通过选择适合的模型参数,运用支持向量机方法建立学习机器,将后续图像帧中的目标从复杂的背景中识别出来,从而实现红外图像分割。实际红外序列图像分割表明,基于支持向量机的图像分割方法不需要复杂的预处理和后处理工作,分割效果理想,对于小目标的图像,识别正确率可达 99%。     

Monitoring nonlinear profile data using support vector regression method

In today's manufacturing industries, if the quality characteristic of a product or a process is assumed to be represented by a functional relationship between the response variable and one or more explanatory variables, then the data generated from such a relationship are called profile data. Generally speaking, the functional relationship of the profile data rarely occurs in linear form, and the real data usually do not follow normal distribution. Thus, in this paper, the functional relationship of profile data is described via a nonparametric regression model and a nonparametric exponentially weighted moving average (EWMA) control chart is developed for detecting the process shifts for nonlinear profile data in the Phase II monitoring. We first fit the nonlinear profile data via a support vector regression model and use the fitted values to calculate the five metrics. Then, the nonparametric EWMA control chart with the five metrics can be constructed accordingly. Moreover, a simulation study is conducted to evaluate the detecting performance of the new control chart under various process shifts using the out‐of‐control average run length. Finally, a realistic nonlinear profile example is used to demonstrate the usefulness of our proposed nonparametric EWMA control chart and its monitoring schemes. It is expected that the proposed nonparametric EWMA control chart can enhance the monitoring efficiency for nonlinear profile data in the phase II study.     

Techniques based on genetic algorithms for large deflection analysis of beams

A couple of non-convex search strategies, based on the genetic algorithm, are suggested and numerically explored in the context of large-deflection analysis of planar, elastic beams. The first of these strategies is based on the stationarity of the energy functional in the equilibrium state and may therefore be considered weak. The second approach, on the other hand, attempts to directly solve the governing differential equation within an optimisation framework and such a solution may be thought of as strong. Several numerical illustrations and verifications with ‘exact’ solutions, if available, are provided For communication