基于减法聚类和自适应神经模糊推理系统的递阶模糊系统的设计

提出了一种设计递阶模糊系统的简易而有效的方法.在得到一个单级模糊系统的基础上,用灵敏度分析法对每一个输入变量的重要性进行排序,从而确定每一级子系统的输入变量.利用减法聚类和自适应神经 模糊推理系统逐级对子系统进行训练.所得到的递阶模糊系统可进一步得到简化.仿真实例证实了设计方法的有效性.     

基于两种色彩空间的颜色选择方法

本文分析和比较了HSI和RGB两种色彩空间的特性，提出了一种基于两种色彩空间，可以实现人机交互的选择颜色的方法，并设计了基于该方法的颜色编辑器，通过对彩色图像处理的实验，结果证明该方法适用而且有效。     

Introduction to the Special Issue on Meta-Learning

Recent advances in meta-learning are providing the foundations to construct meta-learning assistants and task-adaptive learners. The goal of this special issue is to foster an interest in meta-learning by compiling representative work in the field. The contributions to this special issue provide strong insights into the construction of future meta-learning tools. In this introduction we present a common frame of reference to address work in meta-learning through the concept of meta-knowledge. We show how meta-learning can be simply defined as the process of exploiting knowledge about learning that enables us to understand and improve the performance of learning algorithms.     

Genetic algorithms combined with discriminant analysis for key variable identification

Many trouble-shooting problems in process industries are related to key variable identification for classifications. The contribution charts, based on principal component analysis (PCA), can be applied for this purpose. Genetic algorithms (GAs) have been proposed recently for many applications including variable selection for multivariate calibration, molecular modeling, regression analysis, model identification, curve fitting, and classification. In this paper, GAs are incorporated with Fisher discriminant analysis (FDA) for key variable identification. GAs are used as an optimization tool to determine variables that maximize the FDA classification success rate for two given data sets. GA/FDA is a proposed solution for the variable selection problem in discriminant analysis. The Tennessee Eastman process (TEP) simulator was used to generate the data sets to evaluate the correctness of the key variable selection using GA/FDA, and the T² and Q statistic contribution charts. GA/FDA correctly identifies the key variables for the TEP case studies that were tested. For one case study where the correlation changes in two data sets, the contribution charts incorrectly suggest that the operating conditions are similar. On the other hand, GA/FDA not only determines that the operating conditions are different, but also identifies the key variables for the change. For another case study where many key variables are responsible for the changes in the two data sets, the contribution charts only identifies a fraction of the key variables, while GA/FDA correctly identifies all of the key variables. GA/FDA is a promising technique for key variable identification, as is evidenced in successful applications at The Dow Chemical Company.     

On neuro-wavelet modeling

We survey a number of applications of the wavelet transform in time series prediction. The Haar à trous wavelet transform is proposed as a means of handling time series data when future data is unknown. Results are exemplified on financial futures and S&P500 data. Nonlinear and linear multiresolution autoregressionmodels are studied. Experimentally, we show that multiresolution approaches can outperform the traditional single resolution approach to modeling and prediction.     

Optimization-based feature selection with adaptive instance sampling

Preprocessing the data to filter out redundant and irrelevant features is one of the most important steps in the data mining process. Careful feature selection may improve both the computational time of inducing subsequent models and the quality of those models. Using fewer features often leads to simpler and easier to interpret models, and selecting important feature can lead to important insights into the application. The feature selection problem is inherently a combinatorial optimization problem. This paper builds on a metaheuristic called the nested partitions method that has been shown to be particularly effective for the feature selection problem. Specifically, we focus on the scalability of the method and show that its performance is vastly improved by incorporating random sampling of instances. Furthermore, we develop an adaptive variant of the algorithm that dynamically determines the required sample rate. The adaptive algorithm is shown to perform very well when applied to a set of standard test problems.     

高压交联聚乙烯电力电缆的选择

主要从电缆导体、金属护套、外护套等方面对高压XLPE电力电缆的选择作了讨论，同时对XLPE电力电缆金属屏蔽层的接地方式及电缆敷设方式进行了分析。     

中性点非有效接地系统单相接地选线技术分析

中国的6kV～66kV配电网广泛采用中性点非有效接地方式,而中性点非有效接地系统单相接地故障选线问题长期以来没有得到很好的解决。针对这一现状完整地归纳了现有的各种选线技术,在阐述选线原理的同时分析了它们的适用条件,并结合现场故障录波数据分析各种选线技术的实际应用效果。分析结果表明,利用故障零序稳态信号进行连续选线的效果要优于其他方法。为了提高选线准确性,提出应充分发挥选线技术之间的互补性,将多种选线技术进行智能化融合。在此基础上,提出选线装置应进一步向准确故障定位的方向发展。     

引黄工程联接段施工方案的比选

从施工组织设计的作用、施工方案制定的内容和评价方法出发 ,指出施工方案制定在施工组织设计中的重要性。通过对山西省万家寨引黄工程联接段 7#隧洞施工方案从进度方面的重点分析研究 ,结合工程质量、进度、投资对工程的影响程度 ,最终选定最优施工方案 ,论证了施工方案制定的重要性。