Research on Contribution Rate Measurement of Education on Economy Based on Fuzzy Neural Network Model

Abstract

junyingtan@163.com Abstract. In this paper, the fuzzy theory and neural network theory to blend together, based on fuzzy neural network model to measure the economic contribution of education, through three stages of index selection and neural network training, the final empirical results show that education for China's economic development has a significant role in promoting. 1. Build the model The impact of the education on economic growth is typical social issues and economic issues. Its complexity makes some deterministic mathematical methods are difficult to apply, even in many cases we cannot build a structural model to determine the relationship between the two. In this case, the fuzzy theory and neural network method is more applicable, especially in the new millennium, in the fuzzy theory to the study of evaluation in education economic contribution application more and more common. Based on this, this paper combined two methods; construct the fuzzy neural network evaluation model, used in education and economic contribution of the research. Positive steps in this article consist of three levels: first, to build three evaluation parameters set. The first set of evaluation parameters including per capita gross national product; the second set of parameters including the evaluation, the per capita gross national product per capita of fixed assets, human capital, per capita arable land; and the third set of parameters including evaluation, education capital index health capital indicators index experienced capital, human capital indicators. Three parameters set have their own functions, the first evaluation parameter set is used to fuzzy classification of various areas in China, the second set of parameters used to evaluate the linkages between human capital and economic, the third set of evaluation parameters used to establish the link between education and human capital. Secondly, based on neural network method and the first evaluation parameter collection, to our country economic development in different areas of the general classification, because the different economic levels in the region, human capital intensity of the economic impact varied widely. Once again, it is built on the economic level of the same area and the connection between the human capitals to the economic growth model. As for the second evaluation parameters collection, with the characterization of per capita GNP, respectively, characterization per capita fixed assets, human capital, per capita arable land, their relationship as follows: 0 1 1 2 2 3 3 = k k k k G w w q w q w q   