The Grey Relational Analysis of Driving Forces for Water Consumed Structure

2012 ◽  
Vol 490-495 ◽  
pp. 1612-1616 ◽  
Author(s):  
Cui Mei Lv ◽  
Fa Xing Du
Keyword(s):  
Grey Relational Analysis ◽  
Driving Forces ◽  
Water Saving ◽  
Utilization Rate ◽  
Sustainable Utilization ◽  
Urbanization Level ◽  
Relational Analysis ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational

Grey Relational Analysis is a method of analysis and calculates the relational degree of evaluated object, which can characterize the relational degree between object with viral object. In this paper it was used to analyze the driving forces of water consumed structure change, and YiChang city was selected as an example. Adopted grey relational degree analysis, the main factors were found out. The results showed that industry water utilization rate, irrigation area, urbanization level are the main driving forces, and corresponding water-saving measures were put forward. This study can provide reference for the construction of water-saving society and sustainable utilization of water resource.

scholarly journals A Calibration-Free Method Based on Grey Relational Analysis for Heterogeneous Smartphones in Fingerprint-Based Indoor Positioning

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3885 ◽  
Author(s):  
Shuai Zhang ◽  
Jiming Guo ◽  
Nianxue Luo ◽  
Di Zhang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Grey Relational Analysis ◽  
Indoor Positioning ◽  
Test Point ◽  
Reference Sequence ◽  
Fingerprint Matching ◽  
Relational Analysis ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational Coefficient ◽  
Grey Relational

The fingerprint method has been widely adopted in Wi-Fi indoor positioning because of its advantage in non-line-of-sight channels between access points (APs) and mobile users. However, the received signal strength (RSS) during the fingerprint positioning process generally varies due to the dissimilar hardware configurations of heterogeneous smartphones. This difference may degrade the accuracy of fingerprint matching between fingerprint and test data. Thus, this paper puts forward a fingerprint method based on grey relational analysis (GRA) to approach the challenge of heterogeneous smartphones and to improve positioning accuracy. Initially, the grey relational coefficient (GRC) between the RSS comparability sequence of each reference point (RP) and the RSS reference sequence of the test point (TP) is calculated. Subsequently, the grey relational degree (GRD) between each RP and TP is determined on the basis of GRC, and the K most relational RPs are selected in accordance with the value of GRD. Finally, the user location is determined by weighting the K most relational RPs that correspond to the coordinates. The main advantage of this GRA method is that it does not require device calibration when handling heterogeneous smartphone problems. We further carry out extensive experiments using heterogeneous Android smartphones in an office environment to verify the positioning performance of the proposed method. Experimental results indicate that the proposed method outperforms the existing ones no matter whether heterogeneous smartphones are used.

scholarly journals Grey Relational Analysis for Hesitant Fuzzy Sets and Its Applications to Multiattribute Decision-Making

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Guan ◽  
Guidong Sun ◽  
Xiao Yi ◽  
Jing Zhao
Keyword(s):  
Decision Making ◽  
Grey Relational Analysis ◽  
Correlation Coefficients ◽  
Hesitant Fuzzy Set ◽  
Fuzzy Measures ◽  
Information Measures ◽  
Relational Analysis ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational

Due to the superiority in expressing the uncertain and vague information, the hesitant fuzzy set (HFS) is regarded as an important tool to deal with multiattribute decision-making (MADM) problems. Quantitative and qualitative fuzzy measures have been proposed to solve such problems from different points. However, most of the existing information measures for HFSs are related to such fuzzy measures as distance, similarity, entropy, and correlation coefficients. The grey relational analysis is omitted. Besides, the existing grey relational analysis for HFSs only considers the range or distance between HFSs data which is only a partial measure of the HFSs. Therefore, in this paper, we improve the grey relational analysis for HFSs and explore a novel slope grey relational degree by considering another factor of HFSs data: the slope. Further, we combine both the distance and slope factors of HFSs data to construct a synthetic grey relational degree that describes the closeness and variation tendency of HFSs simultaneously, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs MADM problems. Finally, combining with two practical MADM examples about energy policy selection and multisensor target recognition, we obtain the most desirable decision results. Compared with the previous methods, the validity, comprehensiveness, and discrimination of the proposed synthetic grey relational degree for HFSs are demonstrated in detail.

scholarly journals Grey Relational Analysis for Hesitant Fuzzy Sets and Interval-Valued Hesitant Fuzzy Sets with Applications to MADM Problems

2017 ◽  
Author(s):  
Xin Guan ◽  
Guidong Sun ◽  
Xiao Yi ◽  
Jing Zhao
Keyword(s):  
Fuzzy Sets ◽  
Grey Relational Analysis ◽  
Hesitant Fuzzy Sets ◽  
Fuzzy Measures ◽  
Relational Analysis ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational ◽  
Interval Valued ◽  
Variation Tendency

Quantitative and qualitative fuzzy measures have been proposed to hesitant fuzzy sets (HFSs) from different points. However, few of the existing HFSs fuzzy measures refer to the grey relational analysis (GRA) theory. Actually, the GRA theory is very useful in the fuzzy measure domain, which has been developed for such the intuitionistic fuzzy sets. Therefore, in this paper, we apply the GRA theory to the HFSs and interval-valued hesitant fuzzy sets (IVHFS) domain. We propose the HFSs grey relational degree, HFSs slope grey relational degree, HFSs synthetic grey relational degree and IVHFSs grey relational degree, which describe the closeness, the variation tendency and both the closeness and variation tendency of HFSs and closeness of IVHFSs, respectively, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs and IVHFSs MADM problems. Finally, combined with two practical MADM examples about energy policy selection with HFSs information and emergency management evaluation with IVHFSs information, we obtain the most desirable decision results, and compared with the previous methods, the validity, effectiveness and accuracy of the proposed grey relational degree for HFSs and IVHFSs are demonstrated in detail.

scholarly journals A Novel TOPSIS Method Based on Improved Grey Relational Analysis for Multiattribute Decision-Making Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Wenguang Yang ◽  
Yunjie Wu
Keyword(s):  
Decision Making ◽  
Grey Relational Analysis ◽  
Average Distance ◽  
Data Distribution ◽  
Main Idea ◽  
Optimal Solution ◽  
Topsis Method ◽  
Relational Analysis ◽  
Relational Degree ◽  
Grey Relational

Multiattribute decision-making (MADM) problem is difficult to assess because of the large number of attribute indices and the diversity of data distribution. Based on the understanding of data dispersion degree, a new grey TOPSIS method for MADM is studied. The main idea of this paper is to redefine the grey relational analysis through the dispersion of data distribution and redesign the TOPSIS by using the improved grey relational analysis. As a classical multiattribute decision analysis method, traditional TOPSIS does not consider the data distribution of the degree of dispersion and aggregation when it is compared with the optimal and worst alternative solutions. In view of the limitations of traditional TOPSIS, this paper has made two major improvements to TOPSIS. Firstly, the new grey relational analysis is applied to evaluate the grey positive relational degree between each alternative and the optimal solution and compute the grey negative relational degree between each alternative and the worst solution. Secondly, the weights of every attribute index about the optimal and worst solutions are put forward based upon the distance standard deviation and the average distance. Finally, the comprehensive grey TOPSIS is utilized to analyze the ranking of weapon selection problem. The numerical results verify the feasibility of the improved grey relational analysis and also highlight the practicability of the grey comprehensive TOPSIS.

Scheme Selection of Camouflage Screen Design Based on Hierarchical Grey Relational Analysis

2013 ◽  
Vol 694-697 ◽  
pp. 2775-2778
Author(s):  
Qi Jia ◽  
Xu Liang Lü ◽  
Zhao Yang Zeng ◽  
Wei Dong Xu ◽  
Jiang Hua Hu
Keyword(s):  
Grey Relational Analysis ◽  
Analytic Hierarchy ◽  
Screen Design ◽  
Relational Analysis ◽  
Correlation Degree ◽  
Relational Degree ◽  
Selection Decision ◽  
Grey Correlation Degree ◽  
Scheme Selection ◽  
Grey Relational

As many influence factors and technical problems should be taken into account in the scheme selection decision of multi-band camouflage screen design, a method based on hierarchical grey relational analysis combing analytic hierarchy process (AHP) and grey relational analysis (GRA) was developed. The hierarchy model was constructed firstly, and the judging matrixes were established through analytic hierarchy process. The weight value for each index which effects the evaluation was determined accordingly. The relational degree between an individual alternative scheme and the ideal alternative and the worst alternative are calculated by grey correlation degree, and the evaluation degree is calculated according the grey correlation degree and weight value. Based on the relational degree a quality order for the alternative schemes can be obtained. Experiment was carried out, which proved that the method mentioned in this paper had both strict mathematical theory basis and good practical utility. This method can be also used in scheme selection decision of other camouflage designs.

Sign in / Sign up

Export Citation Format

Share Document