模拟退火算法与遗传算法结合用于变量筛选

在传统的遗传算法中引入Ｍｅｔｒｏｐｏｌｉｓ接受准则,结合有序Ｇｒａｍ－Ｓｃｈｍｉｄｔ正交化,可以得到预报能力较强的模型,即ＰＲＥＳＳ值较低的模型。该法用于处理钢中微量元素及热处理工艺条件与钢的力学性质关系的问题,并与传统的遗传算法进行了比较,得到满意的结果。     

遗传算法用于变量筛选

利用遗传算法的优越搜索寻优特性,结合有序Gram-Schmidt正文化及PLS算法可得到预报能力较强的模型,即PRESS(预报残差平方和)值较低的模型.该法可用于处理构效关系及人发微量元素与性别关系问题,并与正交递归选择法及逐步回归正向选择法进行比较,结果良好.     

选择岭回归参数k值的改进方法及其在农药定量构效关系中的应用

提出改进的预报相对误差法选择岭回归参数ｋ，以平均预报相对误差替代预报残差平方和，并抑制过拟合。该方法应用于苯乙酰胺类除草农药定量构效关系的二次建模，效果良好，预报精度优于残差平方和方法。     

人工神经网络—紫外光谱定量多组分体系的研究

本文系统地考察了人工神经网络（ＡＮＮ）－紫外光谱（ＵＶＳ）同时定量多组分混合溶液时参数选择时网络训练和预报性能的影响。合理选取诸参数，可提高训练效率改善预报性能，而且所优化的参数集可移植到其它相似体系。     

人参挥发油的提取和分析

应用ＧＣ－ＭＳ－ＤＳ联机分析人参挥发油是当前较好的方法，但人参挥发油的提取方法和ＧＣ－ＭＳ条件的选择明显影响人参挥发油这一复杂天然混合物的分离和鉴定。本工作严格控制提取条件，提高了挥发油收率，达０．９５％；选择最佳ＧＣ－ＭＳ条件，鉴定出７６种化合物，该法稳定重现性好。     

甲基睾酮人体代谢物的选择反应监控检测法

采用ＧＣ／ＭＳ／ＭＳ的选择反应监控（ＳＲＭ）法检测尿中甲基睾酮的人体代谢物，与ＧＣ／ＭＳ的选择离子检测（ＳＩＭ）法比较，ＳＲＭ法有更高的选择性和确证能力。该种检测方法的建立，可以更有效地判定阳性尿。     

稀土复氧化物电阻率分类预报的专家系统

利用模式识别－化学键参数法总结稀土复氧化物电阻率与有关特征参数间的关系，在此基础上建立了一个能检索和预报该类化合物电阻率的专家系统－ＲＥＣＯＲＥＳ。该专家系统具备一般专家系统的基本特点，且包含模式识别程序包，为知识的存储、表达和获取提供了直观而有效的手段。     

分散法制备的CuCl／ZSM—5分子筛催化上C3H6选择性催化还原NO …

研究了由分散法制备的ＣｕＣｌ／ＺＳＭ－５分子筛催化剂上丙烯在过量氧存在下选择催化还原ＮＯ反应，发现该法能使活性组份高度分散于载体上，且所制备的高负载量ＣｕＣ；／Ｈ－ＺＳＭ－５与离子交换法制备的Ｃｕ－ＺＳＭ－５相比在较低反应温度下具有更高的反应活性。     

比较分子场分析研究哒嗪酮的体系的三维构效关系

用比较分子场分析（CoMFA）法对两个体系的化合物进行了研究，得到了预报能力较强的模型. 初步的研究表明，作为一种三维定量构效（3D-QSAR）方法，它能够揭示分子三维结构对活性的贡献，有较广阔的应用前景．     

甲氧苄胺嘧啶药物的非破坏分析

将偏最小二乘（ＰＬＳ）法同近红外漫反射光谱法结合，非破坏分析了粉末药品甲氧苄胺嘧啶。讨论了波长对ＰＬＳ定量预报能力的影响。校正样品和预测样品的预测结果相对标准误差分别为０．３３％和１．３９％。     

Variable-weighted least-squares support vector machine for multivariate spectral analysis

Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method.     

Comparative Study of Variable Selection Using Genetic Algorithm with Various Types of Chromosomes

In this study,different methods of variable selection using the multilinear step-wise regression(MLR) and support vector regression(SVR) have been compared when the performance of genetic algorithms(GAs) using various types of chromosomes is used.The first method is a GA with binary chromosome(GA-BC) and the other is a GA with a fixed-length character chromosome(GA-FCC).The overall prediction accuracy for the training set by means of 7-fold cross-validation was tested.All the regression models were evaluated by the test set.The poor prediction for the test set illustrates that the forward stepwise regression(FSR) model is easier to overfit for the training set.The results using SVR methods showed that the over-fitting could be overcome.Further,the over-fitting would be easier for the GA-BC-SVR method because too many variables fleetly induced into the model.The final optimal model was obtained with good predictive ability(R2 = 0.885,S = 0.469,Rcv2 = 0.700,Scv = 0.757,Rex2 = 0.692,Sex = 0.675) using GA-FCC-SVR method.Our investigation indicates the variable selection method using GA-FCC is the most appropriate for MLR and SVR methods.     

Ensemble Regression Coefficient Analysis for Application to Near-Infrared Spectroscopy

A new variable selection method called ensemble regression coefficient analysis is reported on the basis of model population analysis. In order to construct ensemble regression coefficients, many subsets of variables are randomly selected to calibrate corresponding partial least square models. Based on ensemble theory, the mean of regression coefficients of the models is set as the ensemble regression coefficient. Subsequently, the absolute value of the ensemble regression coefficient can be applied as an informative vector for variable selection. The performance of ensemble regression coefficient analysis was assessed by four near infrared datasets: two simulated datasets, one wheat dataset, and one tobacco dataset. The results showed that this approach can select important variables to obtain fewer errors compared with regression coefficient analysis and Monte Carlo uninformative variable elimination.     

多变量判别分析用于癌症诊断研究

用感应耦合等离子体原子发射光谱及石墨炉原子吸收光谱仪测定了正常人及癌症病人头发样品中15种元素的含量。所得数据用多元多项式扩展增维和逐步回归变量压缩技术以及PLS方法处理,得到了病人与正常人分类极为清晰的二维判别图。据此可将头发用作癌症临床诊断中的分析样品以取代血液样品。     

八极杆碰撞/反应池(ORS)-电感耦合等离子体质谱(ICP-MS)法测定复杂矿物中的稀土元素

对未知复杂矿物样品采用扫描电子显微镜-能谱法解析样品,确定基本成分后选择合适的样品前处理方法。实验中采用过氧化钠熔融法熔解样品,用水浸出,硝酸酸化后制备待测样品溶液。以Rh为内标,用八极杆碰撞/反应池(ORS)-电感耦合等离子体质谱(ICP-MS)法测定了复杂样品中的稀土元素。多次测定同一混合标准溶液结果的相对标准偏差(RSD,n=11)小于4%,加标回收率为90%~110%。方法适用于未知复杂矿物中稀土元素的测定。