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⎡ − ⎢ ⎢ ( s4,0.02) GP = ⎢( s1,0.42) ⎢ ⎣( s3,0.35) ( s , −0.02) 4 − ( s ,0.19) 3 ( s ,0.03) 4 ( s , −0.42) 7 ( s , −0.19) 5 − ( s ,0.17) 5 ( s5, −0.35) ⎤ ( s − ⎥ 4, 0.03) ⎥ ( s , −0.17) ⎥ 3 ⎥ − ⎦ Using RMM(Row Mean Method), the integrated assessment on each indicator was calculated and the comprehensive value of the organization capability, customer relation management, quality of employee, the sustainability was s ,0.07),( s ,0.25),( s ,0.48),( ,0.18) . ( 5 4 2 s4 Ⅵ.. CONCLUSION A new method was used to designate the weight of each element for each expert under group decisionmaking based on linguistic 2-tuple representation. The basic idea for calculating the weight is that the consistency of each element is not the same, thus the decision power of the decision-maker should not be given the same priority. The method was used in building the non-financial performance system for listed company in Chinese SME board. ACKNOWLEDGMENT This work is supported in part by Jiaxing College Education Research Foundation Grant by 85150932 and Economic Commence Market Application Technology Foundation Grant by 2007gdecof004. REFERENCES [1] .D Ben-Arieh, .Z Chen, “Linguisitc group decisionmaking:opinion aggregation and measures of consenus”, Fuzzy Optim Decis Making,2006,5:371-386 [2] Chen Z F, “Consensus in group decision making under linguistic assessment”, Manhattan Kansas, Kansas State University, 2005 [3] Lei Yang, “Group decision making theory and itsapplication—research on aggregating personal preferences into group decision”, Beijing:Economic Science <strong>Publisher</strong>,2004,pp.67-80 [4] F. Ye, Z.J Hong, “ Congregating of the experts' weights for group decision-making problem with incomplete information”, Communication On Applied Mathematics and Computation, Vol2020,pp.63-67, Jan2006 [5] C.H Yu , Z.P Fan, “Maximal tree clustering method based on dyadic semantic information processing”, Systems Engineering and Electronics, Vol28, pp.1519-1522, October 2006 [6] Fedrizzi, “Consensus reaching via a GDSS with fuzzy majority and clustering of preference profiles”, Annals of Operations Research, Vol.51,pp. 127-139, Septermber 1994 [7] Y.F Zhou, F.J Wei, “The Method for Determining the Posterior Weight of Expert Based on Fuzzy Judgment Matrices”, Chinese Journal of Management Science, Vol.14, pp.71-75, March 2006 [8] E Herrera-Viedma,F Chiclana, F Herrera , S Alonso, “Group decision-making model with incomplete fuzzy preference relations based on additive consistency”, IEEE Transactions on systems, man, and cybernetics-part B: cybernetics, Vol.37, pp. 176-189, Jan [9] F.Herrera , L.Martinez, “The 2-tuple linguistic computational model—advantages of its linguistic description, accuracy and consistency”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol.9, pp. 33-48, September 2003 [10] Z.W Gong, S.F Liu, “Linguisitc 2-tuple group decision making method based on different preferences judgment matrices”, System enginerring, Vol.22, pp. 185-189, March 2007 [11] X.W Liao, H Li, G.M.Dong., “A Multi-attribute Group Decision-making Approach Dealing with Linguistic Assessment Information,” Theory and Practice of System Engineering, Vol.9, pp.90-98, September 2006 72
ISBN 978-952-5726-09-1 (Print) Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010, pp. 073-076 Improving Prediction Of Model Gm (1, 1) Based On Class Ratio Modeling Method Qin Wan 1 , Yong Wei 2 , and Shuang Yang 3 1 College of Mathematics and Information, China West Normal University, NanChong, SiChuan, 637009, China 2 College of Mathematics and Information, China West Normal University, NanChong, SiChuan, 637009, China 3 College of Mathematics and Information, China West Normal University, NanChong, SiChuan, 637009, China wanqin1014@126.com, 3306866@163.com,yangshuang_cwnu@163.com Abstract—This article introduces a new method of how to find suitable class ratio according to the class ratio modeling method thought. It causes the class ratio modeling method to be used on the non-steady primitive sequence which has non-homogeneous grey index law directly. And it both extends the application scope of the class ratio modeling method, and avoids the tedious data pretreatment process effectively while improving prediction precision of model GM (1, 1). Index Terms — Class ratio modeling , GM (1, 1) ,Weakening , Buffer operator I. INTRODUCTION Since Mr. Deng Julong has proposed the grey system theory, the application of grey model spreads many domains. Grey model has more advantages compared to traditional prediction method because that grey model has the characteristics of few sample data required, easy calculation, and high prediction accuracy in short terms etc. However in practical application, people discovered that model GM (1, 1) is suitable for slowly increasing data, but its fitting effect with quickly increasing data is unsatisfactory. Professor Liu Sifeng had theoretically proven that the applicable scope of development coefficient a is limited to ( − 2 , 2 ) , and the effective range of it is narrower [ 1] . Therefore, many scholars have made improvement and optimization on grey model from different angles. For instance, many references expand the applicable scope of development coefficient a by reconstructing background value or optimizing grey derivative to improve simulating and predicting precision of grey model, and achieve good results. Because the process of obtaining a by solving grey differential equation is just making whitenization estimation on a,the author in reference [2] puts forward class ratio modeling method of single consequence in [Key research projects] A project supported by scientific research fund of Sichuan Education Department.(2006A007) and basic application research fund of Sichuan (2008JY0112).,and a project supported by china west normal university item(08B032). Wan Qin: teaching assistant of College of Mathematics and Information , China West Normal University. major study is Grey System Analysis. Tel:18990874811. E-mail: wanqin1014@126.com grey system based on this idea.The primary data sequence is not suitable for establishing grey model directly if it could not pass feasibility test, so we can make data preprocessing such as carrying buffer [3] operator on the primitive behavior data sequence before establishing model GM(1,1) on it. Reference [4] propose using weakening buffer operator to affect the primitive behavior data sequence which possesses characteristics: the front part grows (weakens) excessively quickly and the latter part grows (weakens) excessively slowly; Reference [5] propose using strengthening buffer operator to affect to the primitive behavior data sequence which has subsequent characteristics: the front part grows (weakens) excessively slowly and the latter part grows (weakens) excessively quickly. Buffer operators can effectively eliminates the disturbance affect to the primary data effectively in the modeling and forecasting process, and improve the predicting precision of model GM (1, 1). For primary data sequence which has above characteristic, this paper introduces a new method of how to find suitable class ratio according to the class ratio modeling method thought. It causes the class ratio modeling method to be used on the non-steady primitive sequence which has non-homogeneous grey index law directly. And it both extends the application scope of the class ratio modeling method, and avoids the tedious data pretreatment process effectively while improving prediction precision of model GM (1, 1). II. SUMMARY OF THE CLASS RATIO MODELING METHOD [2] Definition As for monotone sequence X = { x( k ) x( k ) > 0, orx ( k ) < 0, k = 1,2,3.... n} ,then x( k −1) σ ( k) = , k ∈ K = { 2,3,... n} is called the back x( k) class ratio (hereinafter using class ratio) at k point of X , and x( k ) τ ( k ) = , k ∈ K = { 2,3,... n} is called x( k − 1) the front class ratio at k point of X .Obviously, the back class ratio and the front class ratio both are the reciprocal value of each other, and both are positive numbers. © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 73
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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TABLE I. DESCRIPTION OF PROPOSITION
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Figure 2. The process of the invers
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Figure 9. (a)the original image.(b)
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In order to reduce to the number of
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that studying being going to be to
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Reference[10] analyzed the evolutio
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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The QoE i (T i ) is the ith user’
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nodes will be formed one cluster, t
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Where n is the number of data point
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Keyboard event Application User mod
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SQL Azure will eventually include a
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The nodes in the suffix tree are dr
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[3] Y. Li, S. M. Chung, and J. D. H
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some drilling fluid produces hydrog
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"normalization", whose membership b
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and minimum structural elements in
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esults of the spatial transform par
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B. Research on protocol actions Com
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ACKNOWLEDGMENT This work is funded
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A. Problem Description In a small b
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[4] Huang, Hung, and J. Y. jen Hsu.
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Further by calculating, the followi
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TABLE II. THE CONCENTRATION BETWEEN
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private key that obtained by using
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ontology, and the domain dictionary
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Figure 2. Example of single-step de
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evocation and the fourth group stor
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focused mainly on rule-based forms
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Ⅵ. CONCLUSION Figure 6. The Flask
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EDCF in comparison with DCF, has so
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B. Simulation results and analysis
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in routing table. When search resou
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others through the selective emotio
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such as weather information, techno
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the opening window, a related page
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1) Process context storage areas. I
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V. CONCLUSION In this thesis, sCPU-
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main types of horizontal search env
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Enterprise Portal security provides
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() t = [ S () t S () t ] T N S ,...
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[2] Kumaravel, N., and Kavitha, V.,
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output, so BP network has been wide
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input to the artificial neural netw
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(2) In a process (tokens from outsi
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all eight normal vectors are identi
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is B object , and the number of emp
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xi, j y' = εα ( (1 + tanh( )) −
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Error 8000 7000 6000 5000 4000 3000
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Architecture (AMBA) a new bus archi
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four layers.The far right of the gr
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to execution time. Also, DBA would
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Liping Chen .......................
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