Forecasting analysis by using fuzzy grey regression model for solving limited time series data

The grey model GM(1,1) is a popular forecasting method when using limited time series data and is successfully applied to management and engineering applications. On the other hand, the reliability and validity of the grey model GM(1,1) have never been discussed. First, without considering other causes when using limited time series data, the forecasting of the grey model GM(1,1) is unreliable, and provide insufficient information to a decision maker. Therefore, for the sake of reliability, the fuzzy set theory was hybridized into the grey model GM(1,1). This resulted in the fuzzy grey regression model, which granulates a concept into a set with membership function, thereby obtaining a possible interval extrapolation. Second, for a newly developed product or a newly developed system, the data collected are limited and rather vague with the result that the grey model GM(1,1) is useless for solving its problem with vague or fuzzy-input values. In this paper the fuzzy grey regression model is verified to show its validity in solving crisp-input data and fuzzy-input data with limited time series data. Finally, two examples for the LCD TV demand are illustrated using the proposed models.     

A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

There are two popular types of forecasting algorithms for fuzzy time series (FTS). One is based on intervals of universal sets of independent variables and the other is based on fuzzy clustering algorithms. Clustering based FTS algorithms are preferred since role and optimal length of intervals are not clearly understood. Therefore data of each variable are individually clustered which requires higher computational time. Fuzzy Logical Relationships (FLRs) are used in existing FTS algorithms to relate input and output data. High number of clusters and FLRs are required to establish precise input/output relations which incur high computational time. This article presents a forecasting algorithm based on fuzzy clustering (CFTS) which clusters vectors of input data instead of clustering data of each variable separately and uses linear combinations of the input variables instead of the FLRs. The cluster centers handle fuzziness and ambiguity of the data and the linear parts allow the algorithm to learn more from the available information. It is shown that CFTS outperforms existing FTS algorithms with considerably lower testing error and running time.     

Determination of temporal information granules to improve forecasting in fuzzy time series

Partitioning the universe of discourse and determining intervals containing useful temporal information and coming with better interpretability are critical for forecasting in fuzzy time series. In the existing literature, researchers seldom consider the effect of time variable when they partition the universe of discourse. As a result, and there is a lack of interpretability of the resulting temporal intervals. In this paper, we take the temporal information into account to partition the universe of discourse into intervals with unequal length. As a result, the performance improves forecasting quality. First, time variable is involved in partitioning the universe through Gath–Geva clustering-based time series segmentation and obtain the prototypes of data, then determine suitable intervals according to the prototypes by means of information granules. An effective method of partitioning and determining intervals is proposed. We show that these intervals carry well-defined semantics. To verify the effectiveness of the approach, we apply the proposed method to forecast enrollment of students of Alabama University and the Taiwan Stock Exchange Capitalization Weighted Stock Index. The experimental results show that the partitioning with temporal information can greatly improve accuracy of forecasting. Furthermore, the proposed method is not sensitive to its parameters.     

Stability analysis and control design of LTI discrete-time systems by the direct use of time series data

This paper proposes a new framework for the stability analysis and feedback control design of LTI discrete-time systems, called the data space approach, in which a set of the time series data generated from a system is solely and directly used for control system analysis and design, instead of using a mathematical model such as transfer function, state equation, or kernel representation. In this approach, system dynamics are represented as a subspace of a finite-dimensional vector space, called the data space, whose vectors correspond to all the subsequences of the time series. Furthermore, this data space is supposed to contain all the dynamic behaviors of the system, hence, by using a basis matrix of the data space, we can establish a data-based stability condition based on the Lyapunov stability theory, which enables us to test the stability of an LTI system directly from its time series data. In addition, for feedback controller design, a data-based stabilizability condition is developed by using a geometrical relationship in data spaces. The original intention of this paper is to introduce the basic idea of the data space approach, hence, for its clarity, all the discussions are carried out under the assumptions of noise-free and a priori known system orders, and only the stabilization problem is considered.     

核电站数字化仪控系统缺省值分析研究

岭澳二期核电站采用数字化仪控系统.为了充分利用数字化技术的优势,开展了DCS缺省值分析工作.在简要介绍了岭澳二期核电站DCS结构和DCS缺省值问题背景的基础上,对DCS缺省值分析方法、分析范围、实现方式和工程应用等方面进行了总结.这对提高仪控系统本身的可靠性和核电站的安全水平具有重要意义.     

互关联后继树在时间序列特征模式挖掘中的应用

在文献[1]中提出的基于互关联后继树（IRST）的时间序列特征模式挖掘方法的基础上,加入了时间窗口的概念,以弥补IRST这种原本应用于文本检索中的索引模型在时间序列应用中的不足.对IRST以及挖掘算法做出了改进,弥补了其只能挖掘出紧密衔接特征模式的缺陷.实验结果表明,该方法可以挖掘出更多更具应用价值的特征模式.     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

核电厂非安全级DCS集成的质量管理

作为核电厂重要的仪控系统,非安全级分散控制系统(DCS)具有功能复杂、内外部接口众多的特点。为了确保核电厂DCS在设计、制造、测试和交付等过程的质量,首先对非安全级DCS的集成过程进行了研究。研究过程采用了IEC 61513标准下系统安全生命周期的模型,分析了在安全生命周期中各阶段执行的相关活动。通过功能安全管理概念的运用,将DCS集成过程的技术管理要求与我国现行的核电厂质量保证要求建立相应的关联,使得两者体系上的融合成为可能。详细探讨了安全生命周期中技术管理要求与核电站质量保证要求之间的对应关系,使得核电厂的质量保证要求能够具体化,同时符合数字化的核电厂仪控系统的特性。最后对非安全级DCS集成质量的管理措施进行了初步的研究。根据核电厂非安全级DCS在工程实施中有别于成熟仪控系统面临的问题,提出了质量管理方法。该方法对于今后新建电厂,特别是新堆型的工程实施具有参考意义。