一种基于高斯过程的非线性PLS建模方法

提出了一种基于高斯过程(GP)和偏最小二乘法(PLS)的非线性PLS方法(GP-PLS),以更加有效地处理过程非线性、多输入和数据共线性等复杂特性,提高模型的推广能力和精度。该方法首先采用PLS进行特征提取,再用GP建立PLS的内部模型,因而具有GP与PLS的优点。对工业丙烯腈生产过程丙烯腈收率软测量建模的应用表明,采用该方法建立的软测量模型在模型精度、推广能力等方面明显优于一些传统软测量建模方法,满足工业现场应用要求。     

基于核函数主元分析的软测量建模方法及应用

提出了基于核函数主元分析(PCA)方法提取变量的特征信息以有效处理非线性数据，并在此基础上进行软测量建模的方法。利用该方法建立了工业萘初馏塔酚油含萘量软测量模型，工业应用结果表明了该方法的有效性和优越性。     

基于IRBFNN和IRAN的非线性动态系统在线自适应建模

将资源分配网络算法(RAN)与相似隐单元合并操作、冗余隐单元删除操作和基于滑动数据窗连接权值学习相结合,形成了改进的资源分配网络(IRAN)算法。IRAN算法用于非线性动态系统的在线建模,能有效地改善模型精度和泛化能力。将改进径向基函数(RBF)神经网络(IRBFNN)和IRAN结合可以用于不确定非线性动态系统自适应建模。仿真研究表明:所提出的建模方法在模型精简、泛化和自适应等方面均具有优良的性能。     

细乳液法制备聚硅氧烷-Ag纳米复合微球及其抗菌性

模糊建模是一种有效的非线性系统建模方法,因为非线性系统的复杂性,仍有很多问题难以处理。针对T-S模糊模型,提出了一种改进的建模及优化方法。首先,将快速搜索密度峰聚类和模糊C均值聚类(FCM)算法相结合,使用快速搜索密度峰聚类算法找到聚类个数和初始聚类中心后,再用FCM算法进行聚类;然后,通过最小二乘法辨识结论参数得到初始T-S模糊模型,使用改进的差分进化(DE)算法整体优化模型的结构和参数,获得最终的T-S模型;最后,选择代表性实例,使用MATLAB程序进行仿真分析和比较,验证了本文方法能有效提高T-S模糊模型的辨识精度和速度。     

工业过程核概率主元模型建立及监控指标

概率主元分析(PPCA)能够根据过程变量的预测误差及其主元的白化值实现对过程的监控。但是PPCA只适合线性过程,而对非线性过程的监控效果不理想。为克服上述缺点,提出一种基于核PPCA(KPPCA)的过程监控方法,定性讨论了KPPCA模型的参数和主元个数选择问题,构造了高维空间的SPE和T2监控指标。该方法利用核函数将非线性数据映射到高维空间,去除了过程的非线性,然后利用PPCA对满足线性关系的过程变量映射值进行监控。仿真结果验证了该方法对非线性过程监控的优越性。     

基于动态旋转角策略的混合量子进化算法在流水车间调度中的应用

针对工业过程中广泛存在的信息不确定性、信息传递的时滞特性以及过程本身的非线性和动态特性,研究了基于不确定数据信息的生产过程系统级建模与过程变量的状态预测方法;提出基于小波互信息的信息传递时延估计方法,建立了概率时延符号有向图模型;采用组合预测方法,实现生产过程中关键变量实时状态的准确预测。在空分过程中进行了应用研究,初步结果表明该方法具有良好的预测精度、实用有效,具有应用推广前景。     

非线性同伦LM算法及在软测量建模中的应用

综合同伦方法与Levenberg-Marquardt(LM)优化方法,提出了一种新型非线性同伦LM神经网络学习算法以改善现有神经网络学习算法的学习效率,分析了不同类型的过渡函数对神经网络泛化性能的影响.该算法具有稳定性强、收敛性能好的特点.结合工业过程实际要求,将提出的改进算法用于丙烯腈收率神经网络软测量建模并与几种常见建模方法比较,结果表明:基于改进算法的软测量模型具有更高的测量精度和更好的泛化性能,满足现场测量要求.     

基于高斯过程和改进教与学优化算法的甲醇合成转化率软测量

针对高斯过程软测量建模过程中,常用的共轭梯度法难以完成高维协方差矩阵的超参数确定等问题,引入了教与学优化算法(TLBO)对高斯过程的训练过程进行了优化,提高了模型训练速度。并对基本的教与学优化算法做出了相应的改进:一是改进了算法的"学生阶段";二是增加了"课外阅读阶段",提高了算法的性能。将这一建模方法应用于甲醇合成转化率测量中,结果表明,该方法具有较好的估计精度。     

住院收费结算维度建模分析

针对医院信息系统中的住院收费主题,选取住院收费结算作为主题业务处理环节,详细描述了采用维度建模技术进行分析设计的过程。根据医院财务管理需求,设计过程将建模技术与需求分析相结合,不仅实现了该业务主题的数据仓库维度建模,同时提高了业务过程中财务管理需求获取的准确度。维度建摸技术在医院信息系统建设过程中的应用将有效提高医院管理目标的实现效率和质量。     

基于辛烷值调合效应模型的在线更新方法及其应用

主要介绍了用于汽油管道调合过程中的辛烷值调合效应模型及模型的在线更新方法。由于汽油辛烷值属性在调合过程中表现出强非线性,使用线性的调合模型会造成较大的预测误差。虽然经典的乙基模型为非线性模型,但是在建模后期或者后期模型维护过程中需要大量的数据,需要做调合实验,往往工作量太大,导致调合工况发生改变时模型很难快速适应。本文提出的基于辛烷值调合效应模型的在线更新方法是以辛烷值调合效应模型为基础,采用递推最小二乘的方法对模型参数进行在线实时更新,从而保证调合效应模型在较大的工况范围内都有效。该方法原理简单、计算速度快,可以适用于不同牌号的汽油调合,已取得了良好的应用效果。     

基于生物信息特征提取的中医辨证

目的伴随着科技的发展,在多学科交叉的今天,选择合适的计算方法对中医证候进行辨证是实现中医规范化的重要任务。方法将多类支持向量机与基于互信息的特征提取方法相结合,用特征提取方法多源数据进行处理,获得信息量最大的症状组合,然后利用这些症状组合作为多类支持向量机的输入进行分类,根据支持向量机的输出就可得到相应的证候。该方法的有效性通过一个中医实例得到证实。结果通过对各种方法获得的症状组合的正确辨证率对比发现,用基于互信息的特征提取方法获得的症状组合较为理想。结论特征提取的结果优于单纯的基于互信息选择症状组合的结果,同时看出支持向量机适合中医证候的小样本研究,客观说明了互信息、特征提取和支持向量机3种方法的结合适用于中医辨证分类。     

一种基于2次核函数支持向量机的多步预测控制算法

提出了一种基于2次多项式核函数支持向量机的多步预测控制方法。通过黑箱辨识和线性化技术得到非线性系统的近似模型,根据预测控制机理,最小化滚动时域的二次型目标函数,利用模型算法控制的方法得到控制器的解析输出。通过一个标准预测模型和一个工业用连续搅拌槽式反应器的模型仿真验证了该控制器的性能,仿真结果表明:该控制器有着良好的预测性能。     

基于SVM的动态建模新方法

为了提高传感器系统的动态特性,本文提出了一种基于支持向量机(SVM)的传感器动态建模新方法。由于支持向量机是基于统计学习理论的新一代机器学习技术,它有效地解决了小样本学习问题,因此本方法建模对实验样本数量没有特殊的要求。最后,仿真实验结果也证明了该动态建模方法在小样本情况下特别有效。     

A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM

The use of machine learning tools has become widespread in medical diagnosis. The main reason for this is the effective results obtained from classification and diagnosis systems developed to help medical professionals in the diagnosis phase of diseases. The primary objective of this study is to improve the accuracy of classification in medical diagnosis problems. To this end, studies were carried out on 3 different datasets. These datasets are heart disease, Parkinson’s disease (PD) and BUPA liver disorders. Key feature of these datasets is that they have a linearly non-separable distribution. A new method entitled k-medoids clustering-based attribute weighting (kmAW) has been proposed as a data preprocessing method. The support vector machine (SVM) was preferred in the classification phase. In the performance evaluation stage, classification accuracy, specificity, sensitivity analysis, f-measure, kappa statistics value and ROC analysis were used. Experimental results showed that the developed hybrid system entitled kmAW?+?SVM gave better results compared to other methods described in the literature. Consequently, this hybrid intelligent system can be used as a useful medical decision support tool.     

Incorporating EBO-HSIC with SVM for Gene Selection Associated with Cervical Cancer Classification

Microarray technology is utilized by the biologists, in order to compute the expression levels of thousands of genes. Cervical cancer classification utilizing gene expression data depends upon conventional supervised learning methods, wherein only labeled data could be used for learning. The previous methodologies had problem with appropriate feature selection as well as accurateness of classification outcomes. So, the entire performance of the cancer classification is decreased meaningfully. With the aim of overcoming the aforesaid problems, Enhanced Bat Optimization Algorithm with Hilbert-Schmidt Independence Criterion (EBO-HSIC) and Support Vector Machine (SVM) algorithm is presented in this research for identifying the specific genes from the gene expression dataset that belongs to cancer microarray. This proposed system contains phases of instance normalization, module detection, gene selection and classification. By Fuzzy C Means (FCM) algorithm, the normalization is performed for eliminating the inappropriate features from the gene dataset. Meanwhile, for effective feature selection, the EBO algorithm is used for producing more appropriate features via improved objective function values. For determining a subset of the most informative genes utilizing a rapid as well as scalable bat algorithm, this proposed method focuses on measuring the dependence amid Differentially Expressed Genes (DEGs) as well as the gene significance. The algorithm is dependent upon the HSIC and was partially enthused by EBO. With the help of SVM classifier, these gene features are categorized very precisely. Experimentation outcomes demonstrate that the presented EBO with SVM algorithm confirms a clear-cut classification performance for the given gene expression datasets. Hence the result provides higher performance by launching EBO with SVM algorithm to obtain greater accuracy, recall, precision, f-measure and less time complexity more willingly than the previous techniques.     

二进制量子粒子群优化算法及其在化工过程故障诊断中的应用

针对实际化工生产过程中故障数据缺乏,采用适合小样本问题的支持向量机(SVM)对化工过程稳态故障进行诊断。为了保证在线故障诊断的实时性,消除高维监控数据以及系统噪声对故障诊断的干扰,提出了一种新的基于二进制量子粒子群优化(BQPSO)算法和SVM的故障特征选择方法。仿真实验表明:BQPSO算法具有良好的全局搜索能力,能够快速、准确地搜索到故障特征变量;而基于特征选择的SVM故障诊断方法能可靠地实现对复杂化工过程的在线故障诊断。     

基于PCA和KPCA特征抽取的SVM网络入侵检测方法

提出一种新颖的基于特征抽取的异常检测方法,应用主分量分析(PCA)和核主分量分析(KPCA)抽取入侵特征,再应用支持向量机(SVM)检测入侵。其中PCA对输入特征做线性变换,而KPCA通过核函数进行非线性变换。利用KDD 99数据集,将PCA-SVM、KPCA-SVM与SVM、PCR、KPCR进行比较,结果显示:在不降低分类器性能的情况下,特征抽取方法能对输入数据有效降维。在各种方法中,KPCA与SVM的结合能得到最优入侵检测性能。     

Down Syndrome Diagnosis Based on Gabor Wavelet Transform

Down syndrome is a chromosomal condition caused by the presence of all or part of an extra 21st chromosome. It has different facial symptoms. These symptoms contain distinctive information for face recognition. In this study, a novel method is developed to distinguish Down Syndrome in a custom face database. Gabor Wavelet Transform (GWT) is used as a feature extraction method. Dimension reduction is performed with Principal Component Analysis (PCA). New dimension which has most valuable information is derived with Linear Discriminant Analysis (LDA). Classification process is implemented with k-nearest neighbor (kNN) and Support Vector Machine (SVM) methods. The classification accuracy is carried out 96% and 97,34% with kNN and SVM methods, respectively. Different from the studies related with the Down Sydrome, feature selection process is applied before PCA according to the correlation between components of feature vectors. Best results are achieved with euclidean distance metric for kNN and linear kernel type for SVM. In this way, we developed an efficient system to recognize Down syndrome.     

Using statistical and machine learning to help institutions detect suspicious access to electronic health records

Objective

To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs.

Methods

From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures.

Results

The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM.

Limitations

The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction.

Conclusion

The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs.