A portfolio selection model using genetic relation algorithm and genetic network programming

In this paper, new evolutionary computation methods named genetic relation algorithm (GRA) and genetic network programming (GNP) have been applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up the most efficient portfolio, the proposed model considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. Then, the selected portfolio is further optimized by the stock trading model of GNP. In a sense, the proposed model is an integrated intelligent model. A comprehensive analysis of the results is provided, and it is clarified that the proposed model can obtain much higher profits than other traditional methods. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于分布估计算法和遗传算法融合的神经网络故障诊断模型研究

本文构造了基于分布估计算法(Estimation of Distribution Algorithm,EDA)和遗传算法(GeneticAlgorithm,GA)融合的神经网络(Neural Network,NN)故障诊断模型。传统的GA看作是对生物进化"微观"层面上的模拟,则EDA是对生物进化"宏观"层面上的建模,是一种全新的进化模式。EDA与GA融合的实质是在解空间"宏观"和"微观"两个层面进行寻优,可克服NN陷入局部最小,提高NN的泛化能力,使故障诊断的容错性能得到有效改善。将该模型用于高压输电线系统的故障诊断,并作容错性能的评估。由仿真测试表明,研究模型的容错性能要优于传统的BP-NN模型和单纯GA优化NN模型。因此,新诊断模型是有一定的理论和实用价值的。     

Estimation of structural changes in nonlinear time series models by using particle filters and genetic programming

Several works have demonstrated detection of changes of state equations (called structural changes) based on statistical measures but have given no suggestions regarding the functional forms of the state equations after changes. This paper deals with the estimation of structural changes in nonlinear time series models by using particle filters, genetic programming (GP), and its applications. We consider the problems of state estimation from the observed time series that are generated based on nonlinear state equations. It is assumed that structural changes can be detected by some measure of likelihood and that the state equation after changes is modified from its current functional form. Individuals corresponding to functional forms in the GP pool are generated at random, and we apply the crossover operation between the current functional form and the individuals by giving possible multiple functional forms. Then, we have the optimal functional form among the possible functional forms generated by GP from the current form. As an application, we show the estimation of structural change for an artificially generated time series and also discuss the estimation of functional forms for a real economic time series before and after structural changes. Copyright © 2015 John Wiley & Sons, Ltd.     

Signal analysis‐based fault classification and estimation of fault location of an unbalanced network using S‐transform and neural network

This paper demonstrates a technique for the diagnosis of the type of fault and the faulty phase on an overhead transmission line, followed by locating the particular fault on the affected phase. The power system network considered in this study is a three‐phase transmission line with unbalanced loading simulated in the PowerSim Toolbox of MATLAB. S‐transform is used to compute the energy components of the voltage signals of the three phases of the transmission line. These features are used as input vectors of a probabilistic neural network (PNN) for fault detection and classification. Detection of the faulty phase(s) is followed by estimation of fault location. The voltage signal of the affected phase is processed to generate the S‐matrix. The frequency components of the S‐matrices for different fault locations are used as input vectors for training a backpropagation neural network (BPNN). The results are obtained with satisfactory accuracy and speed. All the simulations have been done in MATLAB environment for different values of fault locations, fault resistances, and fault inception angles. The effect of noise on the simulated voltage signals has been investigated. The analysis has been further extended by implementing the proposed method in a modified version of IEEJ West 10 machine system model. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于CIM的配电网GIS数据模型存储与维护方法研究

选定CIM模型中的核心、电线、拓扑、量测等包中的相关类和关系作为构建配电网GIS数据模型的CIM子集,并对空间数据交换模型进行了阐述。采用关系型数据库实现配电网GIS空间数据库的设计,将数据模型中的泛化、聚合和简单关联映射为关系型数据库的表间引用关系。提出的数据模型存储方法避免了类间关联全部下落于叶节点类,降低了叶节点对象之间关系的复杂度。通过对配电网GIS数据模型中的类进行适当扩展,为其添加数据维护操作和必要的属性扩展,进而生成数据维护命令,使数据和关系维护变成类对象自身的责任。通过为导电设备类增加Mount和Unmount操作来封装对导电设备的安装和拆除过程,生成拓扑维护命令,改变导电设备安装或拆除后的网络电气连接关系。实例研究表明,所提出的GIS数据模型存储与维护方法降低了配电网对象之间关系维护的复杂度。