Construction mechanism of whitenization weight function and its application in grey clustering evaluation

The clustering evaluation can be used to scientifically classify the objects to be evaluated according to the information aggregation of various evaluation rules. In grey weighted clustering evaluation, the index clustering rule relies on the construction of the whitenization weight function, while the existing construction method of the linear function lacks the construction mechanism analysis and validity explanation. A normative construction principle is put forward by analyzing the construction mechanism of the function. Through proving the normative principle of the function,the basic modal function(BMF) is proposed and characterized by different function forms. Then, a new type of the whitenization weight function and its grey clustering evaluation model algorithm are given by studying the mechanism and nature of the construction of different forms of the function. Finally, the comparative study for self-innovation capability of defense science and technology industry(DSTI) is taken as an example. The results show that the different construction ways of the function have an effect on the clustering result. The proposed construction mechanism can better explain the index clustering rules and evaluation effectiveness,which will perfect the theoretical system of grey clustering evaluation and be applied to practice effectively.

Grey GM(1,1) Model with Function-Transfer Method for Wear Trend Prediction and its Application

Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.

Wind farm active power dispatching algorithm based on Grey Incidence

This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching(WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of highspeed turbines is effectively reduced.

CALCULATION FOR KERNEL OF INTERVAL GREY NUMBER BASED ON BARYCENTER APPROACH

The kernel of interval grey number is most likely the real number,which can be used to represent whitenization value of interval grey number.A novel method for calculating kernel of interval grey number is constructed based on the geometric barycenter of whitenization weight function in the two-dimensional coordinate plane,and the calculation of kernel is converted to the calculation of barycenter in geometric figures.The method fully considers the effect of all information contained in whitenization weight function on the calculation result of kernel,and is the extension and perfection of the existing methods in the scope of application.

Grey Repairable System Analysis

In this paper, we systematically discuss the basic concepts of grey theory, particularly the grey differential equation and its mathematical foundation, which is essentially unknown in the reliability engineering community. Accordingly, we propose a small-sample based approach to estimate repair improvement effects by partitioning system stopping times into intrinsic functioning times and repair improvement times. An industrial data set is used for illustrative purposes in a stepwise manner.

Biological Network Modeling Based on Hill Function and Hybrid Evolutionary Algorithm

Gene regulatory network inference helps understand the regulatory mechanism among genes, predict the functions of unknown genes, comprehend the pathogenesis of disease and speed up drug development. In this paper, a Hill function-based ordinary differential equation (ODE) model is proposed to infer gene regulatory network (GRN). A hybrid evolutionary algorithm based on binary grey wolf optimization (BGWO) and grey wolf optimization (GWO) is proposed to identify the structure and parameters of the Hill function-based model. In order to restrict the search space and eliminate the redundant regulatory relationships, L1 regularizer was added to the fitness function. SOS repair network was used to test the proposed method. The experimental results show that this method can infer gene regulatory network more accurately than state of the art methods.

Hybridized Intelligent Neural Network Optimization Model for Forecasting Prices of Rubber in Malaysia

Rubber producers,consumers,traders,and those who are involved in the rubber industry face major risks of rubber price fluctuations.As a result,decision-makers are required to make an accurate estimation of the price of rubber.This paper aims to propose hybrid intelligent models,which can be utilized to forecast the price of rubber in Malaysia by employing monthly Malaysia’s rubber pricing data,spanning from January 2016 to March 2021.The projected hybrid model consists of different algorithms with the symbolic Radial Basis Functions Neural Network k-Satisfiability Logic Mining(RBFNN-kSAT).These algorithms,including Grey Wolf Optimization Algorithm,Artificial Bee Colony Algorithm,and Particle Swarm Optimization Algorithm were utilized in the forecasting data analysis.Several factors,which affect the monthly price of rubber,such as rubber production,total exports of rubber,total imports of rubber,stocks of rubber,currency exchange rate,and crude oil prices were also considered in the analysis.To evaluate the results of the introduced model,a comparison has been conducted for each model to identify the most optimum model for forecasting the price of rubber.The findings showed that GWO with RBFNN-kSAT represents the most accurate and efficient model compared with ABC with RBFNNkSAT and PSO with RBFNN-kSAT in forecasting the price of rubber.The GWO with RBFNN-kSAT obtained the greatest average accuracy(92%),with a better correlation coefficient R=0.983871 than ABC with RBFNN-kSAT and PSO with RBFNN-kSAT.Furthermore,the empirical results of this study provided several directions for policymakers to make the right decision in terms of devising proper measures in the industry to address frequent price changes so that the Malaysian rubber industry maintains dominance in the international markets.

GM(1,1)建模机理与应用条件分析及其改进方法

GM(1,1)拟合的原始序列为非负齐次指数函数,对任何呈指数变化的序列x(k),可采用x^(0)(k)=x(k)—M或x^(0)(k)=M—x(k)将其转换为非负齐次指数函数变化。GM(1,1)建模的背景值生成Z^(1)=xx^(1)(k)+(1—α)x^(1)(k+1),应满足α=1/α—1/(e^0—1)。当|α|较小时,α非常接近0.5,但当|α|较大时,α偏离0.5值较大,这是在|α|较大时GM(1,1)传统建模方法失效的原因。文中基于建模机理与应用条件的分析,提出了改进的计算方法。

生产中的模糊贝叶斯决策模型

生产中的模糊贝叶斯决策模型李容华（湖南财经学院）一、问题的提出在统计决策中，人们通常用先验概率作决策．这种决策是根据历史资料过经验判断而得到的．实践证明，这种决策难以准确地反映生产现实，具有局际性．我们如果能在其决策中补充现实中发生的新信息，新资料，...

Hybrid customer requirements rating method for customer-oriented product design using QFD

Quality function deployment （QFD） is a well-known customer-oriented product design methodology. Rating the final importance of customer requirements （CRs） is really a very es- sential starting point in the implementation of QFD, since it largely affects the target setting value of design requirements. This pa- per aims to propose a novel method to deal with the relative importance ratings （RIRs） of CRs problem considering customers＇ diversified requirements and unknown information on customers＇ weights, which is an indispensable process for determining the final importance ratings of CRs. First, a new concept of customer＇s assessment structure is proposed according to the basic idea of grey relational analysis （GRA）, and then a constrained nonlinear optimization model is constructed to describe the assessment information aggregation factors of CRs considering customers＇ personalized and diversified requirements. Furthermore, an im- mune particle swarm optimization （IPSO） algorithm is designed to solve the model, and the weight vector of customers is obtained. Finally, a car door design example is introduced to illustrate the novel hybrid GRA-IPSO method＇s potential application in deter- mining the RIRs of CRs.

Decision-oriented Usability Evaluation of an Operation Interface: Model and Application

To support multi-factor decision problems about usability evaluation, especially when studies fall short of comparable objects, a fuzzy synthetic evaluation model is explored in this paper. Grey relational analysis （GRA） is brought in the model to calculate weight vectors of the usability factors. And membership functions of a remark vector are constructed in the context of use of the operation interface. The present method is applied in usability evaluation of operation interface and is proved to be effective. The comprehensive usability gradation of the operation interface to good is 0. 616 4 that meets the requirements in practice.

Grey relation function between local electrical potential and stomatal behavior during light exposure in plants

The stoma of the plant leaf and the factors that elicit movement of its guard cells are taken as a grey system. Effects of temporary light exposure on local electrical potential of the leaf surface (LEP), on CO 2 and on relative humidity (RH) changes within the leaf chamber were recorded with a multi-channel electronic recorder. The results are analyzed by employing the grey relation function ( ξ ) and grey relation grade ( R ) based on the Grey Theory. The analysis shows that LEP could represent the dominant factor in executing the stomatal movement under the light exposure.

内算子值函数的可实现性(英文)

本文讨论内算子值函数的态空间实现问题 .证明了任何一个内算子值函数都具有正合的无限时间可控和正合的可观测的适定的系统实现 .在此基础上 ,我们还得到相关C0 半群为指数稳定的或成为一个群的充分必要条件 ,从而推广了 [3]中的相应结果 .

Grey clustering evaluation based on AHP and interval grey number

Purpose–The purpose of this paper is to propose a grey clustering evaluation model based on analytic hierarchy process(AHP)and interval grey number(IGN)to solve the clustering evaluation problem with IGNs.Design/methodology/approach–First,the centre-point triangular whitenisation weight function with real numbers is built,and then by using interval mean function,the whitenisation weight function is extended to IGNs.The weights of evaluation indexes are determined by AHP.Finally,this model is used to evaluate the flight safety of a Chinese airline.The results indicate that the model is effective and reasonable.Findings–When IGN meets certain conditions,the centre-point triangular whitenisation weight function based on IGN is not multiple-cross and it is normative.It provides a certain standard and basis for obtaining the effective evaluation indexes and determining the scientific evaluation of the grey class.Originality/value–The traditional grey clustering model is extended to the field of IGN.It can make full use of all the information of the IGN,so the result of the evaluation is more objective and reasonable,which provides supports for solving practical problems.

Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature

The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

The Further Development and Application of Grey Forecasting Model

In this paper, the method which forecasts original sequences {x (0)(k)} with logarithmic function or with power function has been complemented, and the method which handles original sequences by logarithmic function-power function transformation or by power function-logarithmic function transformation has been presented, then smooth degree and precision of forecasting of discrete data have been improved.

基于二维区间灰数的核与灰度的构造研究

目前关于区间灰数的研究主要集中在一维区间灰数,但是实际生活中,单一区间灰数很难全面认识一个研究对象,二维区间灰数相较于一维区间灰数,能更全面的表征研究对象的灰色特征。对此,文章将区间灰数从一维拓展到二维,构造了二维区间灰数的核与灰度。针对在可能度函数已知的情形下,给出了二维区间灰数以及核与灰度的定义及其基本运算法则,并对区间灰数间并的核与灰度定义及相关性质进行探究。探讨当区间灰数具有典型可能度函数时相应的核与灰度,并给出相关算例和实际案例。

白化权函数已知的区间灰数的核与灰度

针对在白化权函数已知的情形下,区间灰数的核与灰度的构造问题进行研究.给出该情形下区间灰数的核与灰度的重构定义,提出区间灰数间并的核与灰度,并对它们的相关性质进行分析.探讨当区间灰数具有典型白化权函数时相应的核与灰度,特别地,当白化权函数退化成在区间灰数内取值为1,即缺乏区间灰数的取值分布信息时,核、灰度的结果与原有定义一致.所提出的核与灰度是对已有研究的一种拓广.

GM(1,1)建模机理与应用条件分析及其改进方法

GM(1,1)拟合的原始序列为非负齐次指数函数。对任何呈指数变化的序列x(k),可采用x^(o)(k)=x(k)-M或x^(o)(k)=M-x(k)将其转换为非负齐次指数函数变化。GM(1,1)建模的背景值生成Z^(1)(k+1)=αx^(1)(k)+(1-α)x_(1)(k+1)应满足α=(1/α)-1/(e~α-1)。当|α|较小时,α非常接近0.5,但当|α|较大时,α偏离0.5值较大,这是在|α|较大时GM(1,1)传统建模方法失效的原因。文中基于建模机理与应用条件的分析,提出了改进的计算方法。