Study on the Performance Indicator Weights of Higher Agricultural Education Resources anagement Based on the Triangular Fuzzy Function Li Zhang 1 , Yan-li Zhao 2， Liu-cheng Zhang 1 1Practical teaching management center in Harbin University of Commerce, Harbin 150028, China 2 College of Management in Harbin University of Commerce, Harbin 150028, China Abstract - The triangular fuzzy function has been constructed based on triangular fuzzy theory, we have analyzed statistical findings of the performance indicators importance for the management of higher agricultural education resources, the triangular fuzzy function has been solved through the method of gravity center, then the fuzzy weights of performance indicators have been determined. Keywords - Higher agricultural education; performance indicators; triangular fuzzy number As an important part of higher education of our country, Higher agricultural education is the key of agricultural education, is the important base of high-level agricultural personnel training, scientific research, technology promotion, industry development and technology innovation. In recent years, our country higher agricultural education realized the leap of reform and development which has a positive impact and role to the national social economy especially the development of the rural economy. However, there are little effective allocation and management mechanism between the limited higher agricultural education resources and the rapidly developed agricultural economy. That brings about the function of higher agricultural education resources supporting economic development can't fully express and causes part effective resources waste. Therefore, to speed up the establishment of efficient management system of higher agricultural education resources and take fully advantages, is not only the knowledge security that effectively putting the development strategy of revitalizing the nation through science and education into effect, but also the important rely which promotes regional characterized economy develop steady and healthy[2-4]. This paper has made statistics and analyzed the investigation result of the importance of the higher agricultural education resource management performance index by the micro angle of the performance management, combining its own characteristics of higher agricultural education resource management and structuring triangular fuzzy function based on the triangle fuzzy theory. Solve the triangular fuzzy function by Center of Gravity Defuzzification and finally determine the fuzzy weights of performance indicators have lay the foundation for the performance evaluation index of the higher agricultural education resources management. Ⅰ RESOURCES MANAGEMENT AND PERFORMANCE EVALUATION FOR THE HIGHER AGRICULTURAL EDUCATION Agricultural education which combines both narrow and broad cents is a kind of social activities to train agricultural talents. General education of agriculture refers to all of activities which spread agricultural science and technology knowledge and train agricultural talents. Special education refers to all kinds of agricultural school education. Higher agricultural education is not only an important part of the entire education system but also a characteristic branch in higher education.  It is a professional education activity concerning about agriculture and the related which is on the basis of the common education. It includes not only higher agricultural education in all levels, but also other education related to the field of agricultural science and technology. From the point of view of the resource property, we can distinguish higher agricultural education resources into the physical resources and the invisible resources. Among them, scientific research management personnel, education facilities and education expenses are included in the physical resources, while agricultural science and technology management knowledge, education skills, atmosphere and interpersonal relationship in the education field of internal and external. Therefore, higher agricultural education resources can be regarded as a collection which higher agricultural education is relied to exist and develop in. It's an integrated system coming from the interaction by personnel, funds, infrastructure, management system, teaching and scientific research. On the basis of human, material and financial resources, through a serious of synthesis applications of management technology, management procedure and management methods, higher agricultural education resources and management make adjustment and optimization on all elements in order to produce the best society benefit. [7-8] In conclusion, higher agricultural education resource management performance evaluation is a process that checks and measures the internal and external effects of higher agricultural education resources and management by various managements and evaluation methods. On the purpose of discovering problem and solving problem timely, higher agricultural education resources and management system makes adjustment and optimization continuously in the changing inner and outer environment, in order to expose on the state of the best social effect. [9-10] performance are included in, they survey each unit (the expert group) for the cognition to the important degree of each performance index. [11-12] The design of the word corresponding fuzzy number is for the standard of seven scale, The design of the semantic variable calculated amount is regarded as triangular fuzzy number, as is shown in table 1. Ninety-nine questionnaires were issued. The expert group made the important degree of fuzzy semantic measure for the first and second stage performance evaluation indexes while withdrawing seventy-seven copies. Combined with uncertainty analysis of the statistical methods of triangular fuzzy numbers, making sure fuzzy weights by the weight analysis, thus achieving each index weight tendency from the expert group, we got triangular fuzzy number forming the expression of the index by the maximum, minimum and intermediate value. Ⅱ THE TRIANGULAR FUZZY NUMBER EXPRESSION OF THE IMPORTANT DEGREE OF THE HIGHER AGRICULTURAL EDUCATION RESOURCE MANAGEMENT PERFORMANCE INDEX In the research, by the application of the triangular fuzzy number, translating the importance of the quantification extent into an analyzable quantitative number, thus eliminating the unreasonable due to personal subjective factors. According to the earlier research achievement about the key factor of higher agricultural education resources and management, among the experts who are engaged in relevant work, aiming at some expert group survey which five first class indexes and twenty-six second class indexes of higher agricultural education resource management Table 1 The Tiangle Symmetrical Fuzzy Number Notation of Higher Agricultural Education Resource Management Performance Evaluation Indexes Weights 2 3 4 6 7 1 Not very Important Fuzzy number Triangular fuzzy umber representation ( 1,1,2) (1,2,3) Establishing fuzzy weights by triangle fuzzy number measuring method, transforming the original quantification of the important extent into the quantitative numerical that can be analyzed, in order to eliminate unreasonable due to personal subjective factors, includes all index importance opinion from the experts, so that we can get more objective and accurate weights in the questionnaire survey results. Not Important (2,3 ,4) Gen eral Imp ortant (3,4 ,5) 5 Impor tant (4,5 ,6) So me Imp ortant (5,6 ,7) Ver y Important ( 6,7,7) Ⅲ THE SATISTICS AND ANALYSIS OF THE RESULTS OF THE SURVEY ABOUT THE IMPORTANCE DEGREE IN HIGHER AGRICULTURAL EDUCATION RESOURCE MANAGEMENT PERFORMANCE INDEX Let's collect and settle the expert group survey. Then, we get some statistic information of higher agricultural education resource management performance index in the importance degree. Making the following data calculation and volume management, we can see the results in table 2 and table 3. Table 2 The Analysis Sheet of the Results of the Survey Statistics About the Importance In Higher Agricultural Education Resource Management Performance Index.(One Class Index) Arith Stand Coeffici Performance S Triangular fuzzy metic mean ard deviation ent of variation indicators td.D weight A human 0.19 0.014 0 0.09137 (0.1734,0.1901,0.2067) resources 01 71 .0031 0.22 0.012 0 (0.2045,0.2221,0.235 B funds 0.08467 21 50 .0026 7) 0.16 0.020 0 (0.1501,0.1631,0.171 C infrastructure 0.1104 31 15 .0036 2) D management 0.15 0.024 0 (0.1428,0.1542,0.164 0.1479 environment 42 32 .0033 3) 0.27 0.011 0 (0.2611,0.2705,0.280 E research output 0.08357 05 29 .0021 9) Table 3 The Analysis Sheet of the Results of the Survey Statistics About the Importance In Higher Agricultural Education Resource Management Performance Index.(Level 2 Index) Arit Sta Var S Triangular fuzzy Performance indicators hmetic ndard iation td.D weight mean deviation coefficient A1 the quantity of teaching and 0 0.0 0 0 (0.1911,0.2043,0.2 scientific research personnel .2043 272 .0885 .0033 104) A2 the proportion of teaching 0 0.0 0 0 (0.2734,0.2815,0.2 and .2815 230 .0912 .0028 916) scientific research personnel A3 master or higher number of 0 0.0 0 0 (0.2447,0.2541,0.2 teaching staff .2541 223 .1043 .0041 615) 0 0.0 0 0 (0.1207,0.1322,0.1 A4 overseas quantity .1322 372 .1012 .0035 483) A5 external research personnel 0 0.0 0 0 (0.1191,0.1279,0.1 quantity .1279 401 .0904 .0021 325) 0 0.0 0 0 (0.2517,0.2607,0.2 B1 per total funding .2607 347 .1002 .0023 757) 0 0.0 0 0 (0.2431,0.2512,0.2 B2 per research funding .2512 350 .0745 .0027 670) 0 0.0 0 0 (0.1942,0.2013,0.2 B3 per education funding .2013 325 .1100 .0033 100) 0 0.0 0 0 (0.1602,0.1720,0.1 B4 per infrastructure funding .1720 400 .1023 .0030 870) B5 total investment funds of 0 0.0 0 0 (0.1100,0.1148,0.1 GDP .1148 350 .0971 .0031 523) C1 the member of university 0 0.0 0 0 (0.2113,0.2258,0.2 with .2258 233 .0701 .0025 309) more than ten thousand people C2 the proportion of Higher 0 0.0 0 0 (0.1621,0.1732,0.1 agricultural education .1732 306 .0839 .0029 846) 0 0.0 0 0 (0.1973,0.2012,0.2 C3 network construction .2012 542 .1007 .0022 107) C4 multimedia classroom 0 0.0 0 0 (0.1645,0.1704,0.1 quantities .1704 297 .0931 .0047 801) 0 0.0 0 0 (0.1076,0.1150,0.1 C5 fixed assets worth .1150 301 .0999 .0031 217) 0 0.0 0 0 (0.1040,0.1144,0.1 C6 intangible assets worth .1144 405 .1012 .0030 200) D1 the constitution of related 0 0.0 0 0 (0.1924,0.2031,0.2 policy .2031 315 .0857 .0020 127) laws and regulations 0 0.0 0 0 (0.1943,0.2015,0.2 D2 constraint and incentive mechanism .2015 364 .0899 .0031 123) D3 the proportion of professional 0 0.0 0 0 (0.1878,0.1906,0.2 management staff .1906 210 .0997 .0021 011) 0 0.0 0 0 (0.1957,0.2013,0.2 D4 relevant administrative departm ents .2013 565 .1300 .0023 122) 0 0.0 0 0 (0.1975,0.2035,0.2 D5 broadcast and training .2035 500 .0955 .0029 123) 0 0.0 0 0 (0.1023,0.1108,0.1 E1 university enrollment .1108 305 .1135 .0041 215) 0 0.0 0 0 (0.2159,0.2217,0.2 E2 graduates .2217 337 .0807 .0022 308) E3 an average annual award for the 0 0.0 0 0 (0.2111,0.2257,0.2 master degree .2257 410 .0999 .0027 310) E4 an average annual above provincial 0 0.0 0 0 (0.2322,0.2407,0.2 scientific research project number .2407 176 .0700 .0031 519) E5 an average annual research 0 0.0 0 0 (0.1933,0.2011,0.2 results award number .2011 299 .1022 .0030 108) Among them, the standard deviation described the degree of dispersion which the expert group evaluated the importance degree of the performance index. The standard deviation much smaller, the importance degree value of the performance index more close to the mean value degree and vice versa. The standard deviation is calculated on the basis of the mean value which impacts the standard deviation. The standard deviation coefficient, the coefficient of variation, represents the differences that the experts approve on the performance index importance. Therefore, we should take the coefficient of variation into reference. The smaller the coefficient that relative degree of difference is smaller, then the degree of dispersion of the performance index importance will be smaller. This suggests that the social identification of the experts in performance index importance more concentration. The standard error: Std. D. < 1. Finally, we structured the range expression of the triangular fuzzy weight of various performance indicators on the basis of maximum, minimum and the intermediate value. In the above research process, because of collecting the weight information through different units of expert group, so it's inevitable that the analysis results will have some degree of variability. Both of the coefficient of variation and standard deviation reflect the concentration trend. That is the representation of the mean arithmetic, the greater the coefficient of variation, the lower the concentration of trend representatives. As shown in table 3, The D4 related management department has the largest standard deviation and coefficient of variation. This is mainly because that the relevant departments in different units are different, and even some units don't separate specialized management. Therefore, different expert groups feel the difference in understanding of this index weight normal. In addition, in Table 3, standard deviation and coefficient of variation of E4 growth research project number above provincial and ministerial level are minimal. That's because the scientific research project plays a vital role in the efficiency of higher agricultural education scientific research project resource management. It's an industry consensus. therefore, different expert groups have few of differences on the degree of cognitive on this index. Ⅳ SOLVING THE FUZZY WEIGHTS OF HIGHER AGRICULTURAL EDUCATION RESOURCES MANAGEMENT PERFORMANCE EVALUATION In order to overcome the uncertainty of the subjective judgment, using fuzzy number to get a clear value can more intuitively reflect the weight of the expert group judgment. Here, the process to determine the weight values is the process to solve fuzzy problems. There are many methods to solve fuzzy problems, without fixed program, as long as some criterions in reasonable, simple in calculation, continuity. The gravity solution to solve fuzzy problem, is a kind of common and rational method. Suppose a fuzzy set for M, the membership functions for X , x X , M is the explicit value DF M i after conversion of the gravity solution to solve fuzzy problem. Then, , among them, the x X dx DF M X dx denominator X is the acreage and DF M is the M x i x M M i shaft position where the center of gravity of the projection is. According to the gravity method, through a group of triangular fuzzy numbers combined with performance index weight maximum value, minimum value and the intermediate value in Table 2 and Table 3, after treatment in defuzzification and uniformization, the fuzzy weight value of the second stage performance index will be shown finally in Table 4. Table 4 The Fuzzy Weight Value Table of Higher Agricultural Education Resource Management Performance Evaluation. weight Lev Level 2 indicator index el 1 A B C D E indicator 0.19 0.201 1 0.2592 0.2215 0.2023 0.1167 10 7 0.22 0.279 2 0.2503 0.1745 0.2021 0.2207 03 0 0.16 0.254 3 0.2017 0.2002 0.1900 0.2212 47 7 0.16 0.132 4 0.1725 0.1709 0.2047 0.2391 07 7 0.26 0.131 5 0.1163 0.1165 0.2009 0.2023 33 9 6 ---0.1164 --- Ⅴ THE EXPERTS SELECTED TENDENCY ANALYSIS OF HIGHER AGRICULTURAL EDUCATION RESOURCE MANAGEMENT PERFORMANCE EVALUATION FUZZY WEIGHTS. 1) Pay Attention to Input and Output Efficiency After the statistical analysis of the recovered questionnaire, we can easily to find in Table 2 that the weight values of investment and research outputs are the maximum, the standard deviation and coefficient of variation are the minimum and the difference of fuzzy weights of the index value in the level 2 index of the level 1 is the minimum, as is shown in Table 3. This indicates that different expert groups have the same attention degree on funding and research output , while the little difference in two grade index further explains the value degree of the expert group in input and output. 2) The Hard Environment and Soft Environment of the Relative Weakening Management In Table 2, one class index shows that different expert groups pay the same attention to the funding and research output. The little difference in two grade index explains the expert groups' value degree for input and output. The fuzzy weight value of infrastructure and management is minimum, while the relative standard deviation is maximum and the coefficient of variation is relatively large. It means that the expert groups have large difference in the recognition between infrastructure and management. The vast majority of expert groups made relative weakening selection on the importance of the five level 1 indicators. This shows that, at present, China's relevant departments relatively neglect the dual role of infrastructure and management in higher agricultural education resource management, while the abroad pay more attention to the double construction of hard and soft environment. 3) Begin to Pay Attention to Academic Exchanges In the retracted questionnaires, individual experts put forward a influence factor called the regional or international academic conference. Through consulting we realize, some areas or units especially some institutions in low level, have little conscious or unconscious to do academic exchanges and cooperation, because of budget and system constraints. This impedes the utilization of higher education resources advances management ideas and methods and the raise of improvement of management level. Although, only individual experts suggest this effect, it's enough to illustrate that our country's higher agricultural education researchers and practitioners have begun to recognize the problem. With the advance of our higher agricultural education resource management, the influencing factors will become the effectively complement to the evaluation index system. Ⅵ CONCLUSION In the process of collecting questionnaires and expert consultation, we found that Chinese researchers and workers urgently needed a set of scientific, reasonable higher agricultural education resources management performance evaluation index system, in order to grasp higher agricultural education resource management better. Due to the late of our country's relevant work, therefore, related research was lagging behind. At present, the relevant domestic research literature on higher agricultural education resources management performance evaluation that can be referred was little. As the determination of higher agricultural education resources management performance evaluation index weight value having many subjective factors, so how to accurately determine the weight of index value is a difficult point in higher agricultural education resource management evaluation index system research. In view of this, on the basis of 26 important indicators screened in the previous work, this article made a large attempt in this field advantage of triangular fuzzy number. Through questionnaires, using a 7 point rating value form, selecting each index from the different expert groups in the same industry to undertake evaluation, determining the expression range of triangular fuzzy value, finally through the center of gravity defuzzification can we get the fuzzy weight value of level 1 indicator and level 2 indicator, thereby to overcome the uncertainty of the subjective judgment effectively. 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