Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels

This paper addresses the supervised learning in which the class memberships of training data are subject to ambiguity. This problem is tackled in the ensemble learning and the Dempster-Shafer theory of evidence frameworks. The initial labels of the training data are ignored and by utilizing the main classes’ prototypes, each training pattern is reassigned to one class or a subset of the main classes based on the level of ambiguity concerning its class label. Multilayer perceptron neural network is employed to learn the characteristics of the data with new labels and for a given test pattern its outputs are considered as basic belief assignment. Experiments with artificial and real data demonstrate that taking into account the ambiguity in labels of the learning data can provide better classification results than single and ensemble classifiers that solve the classification problem using data with initial imperfect labels.     

Tree ensemble construction using a GRASP-based heuristic and annealed randomness

Two new methods for tree ensemble construction are presented: G-Forest and GAR-Forest. In a similar way to Random Forest, the tree construction process entails a degree of randomness.The same strategy used in the GRASP metaheuristic for generating random and adaptive solutions is used at each node of the trees. The source of diversity of the ensemble is the randomness of the solution generation method of GRASP. A further key feature of the tree construction method for GAR-Forest is a decreasing level of randomness during the process of constructing the tree: maximum randomness at the root and minimum randomness at the leaves. The method is therefore named “GAR”, GRASP with annealed randomness.The results conclusively demonstrate that G-Forest and GAR-Forest outperform Bagging, AdaBoost, MultiBoost, Random Forest and Random Subspaces. The results are even more convincing in the presence of noise, demonstrating the robustness of the method.The relationship between base classifier accuracy and their diversity is analysed by application of kappa-error diagrams and a variant of these called kappa-error relative movement diagrams.     

Deriving reservoir operating policies using classifier systems

An application of classifier systems to time variant, water resources allocation problems is described, the aim of the work being to assess the suitability of the technique for deriving control strategies. The difficulties of allocating credit to classifiers where co-operating sequences of rules have to be developed are discussed and methods for overcoming some of the difficulties are covered. The training of the classifier on two problems, the first containing a single, surface water reservoir and the second two reservoirs is used to develop techniques and the results show that classifiers possess an ability to learn about the domain. However the resulting operating strategies are not appropriate for the operation of water resources systems. The work indicates that in their current format, classifier systems cannot learn to operate systems where long, interdependent chains of decisions are involved.     

级联式天然气液化工艺模拟研究

主要对级联式天然气液化流程进行研究,并通过工艺模拟软件对级联式天然气液化流程进行模拟分析。通过分析得出级联式天然气液化工艺是实用可靠的天然气液化工艺。该工艺具有技术成熟、操作稳定以及能耗低的优点,适合于小型基本符合型天然气液化工厂。     

斜窄上升流过程与单元集成式分离设备

拉美拉斜板以及早年的Lavodune/Ladoflux分离设备,应用的都是斜窄上升流过程,但前者是单元集成结构,而后者是单个设备。介绍了在拉美拉的基础上,对斜窄流过程及其单元集成设备结构特征的系统研究以及斜窄流浓缩、分级、沉渣-除油等分离设备及其在矿业多种领域的实际应用。     

Supervised data analysis and reliability estimation with exemplary application for spectral data

The analysis and classification of data is a common task in multiple fields of experimental research such as bioinformatics, medicine, satellite remote sensing or chemometrics leading to new challenges for an appropriate analysis. For this purpose different machine learning methods have been proposed. These methods usually do not provide information about the reliability of the classification. This, however, is a common requirement in, e.g. medicine and biology. In this line the present contribution offers an approach to enhance classifiers with reliability estimates in the context of prototype vector quantization. This extension can also be used to optimize precision or recall of the classifier system and to determine items which are not classifiable. This can lead to significantly improved classification results. The method is exemplarily presented on satellite remote spectral data but is applicable to a wider range of data sets.     

人工神经网络和模式识别

80年代重新兴起的人工神经网络已成为世界人工智能研究的热门课题之一。本文介绍了人工神经网络的一般结构及其算法,着重介绍人工神经网络在模式识别方面的作用及用作模式识别的人工神经网络分类器。希望能给读者了解这一当今世界科学前沿热门课题起一介绍作用。     

逆变器短路控制式串级调速系统在发电厂排煤粉机90kW绕线式异步电动机上的应用

本文介绍了逆变器短路控制式串级调速系统在本钢发电厂排煤粉机00kW绕线式异步电机上的应用,文中介绍了该串调系统的工作原理,实际构成,工业实验结果及体会。     

串级调速双闭环系统动态响应的计算机分析及综合

交流串级调速双闭环系统的动态结构,虽然与他励直流调速系统具有基本相似的结构形式,但却有值得注意的特点,即是它实际运转在不同转差或不同负载条件下,而会引起电流环和转速环分别偏离“二阶最佳”及“三阶最佳”状态。这些偏离的程度,综合电流环的转差率和综合转速环的负载电流等的适当值,为实现最佳状态下运行,则都是问题。对这些问题有关文献说法不一。本文通过计算机计算分析,获得上述问题的结论。     

混流式转轮近下环叶栅水洞实验研究

本文介绍了混流式转轮ＨＬ２２０近下环三组叶栅（圆柱展开的实测直列叶栅、设计叶栅及改进叶栅）的水洞能量和汽性能的实验研究成果，分析了三组叶栅叶型细微差别及其对应的能量汽蚀性能之间的区别，阐述了采用快速、经济的叶栅水洞实验研究来有效改善ＨＬ２２０转轮近下环区域的能量、汽蚀特性的原理和方法。