模糊软分类算法及教育贡献率的测算方法研究

发布时间：2018-06-18 23:18

本文选题：模糊软聚类算法 + GA-CFO-CFM算法　；参考：《西安科技大学》2017年硕士论文

【摘要】：近年来中国的经济发展所遇到的瓶颈与问题显示除了依靠资本、劳动等生产要素投入的增加外,凭借技术进步提高生产要素使用效率进而提高劳动生产率可以促进经济持续增长。同时,人力资本理论表明,教育一方面可以促进技术的进步,另一方面可以带来人力资本的积累,因此大力发展教育尤其是作为教育最高层次的高等教育成为助推科技进步、引领经济结构转型升级的必经之路。首先通过对比了中国东西部之间以及与国外发达国家高等教育人力,资金各方面的投入,肯定我国高等教育目前已取得的成绩同时寻找依然存在的问题和弊端。其次,设计了一类改进的模糊软聚类算法:新算法将遗传算法、中心引力算法和模糊聚类算法融合在一起,通过遗传算法自适应的确定分类数,中心引力算法优化准则函数,模糊聚类算法确定样本数据在每一类中的隶属度。新算法可以自适应的确定样本数据的分类数,从而实现无监督的分类。以中西部地区十九省的经济数据为样本,通过改进的模糊软聚类算法,将中西部十九省份划分为三类,使同一类省份高等教育的贡献率比对更加科学、合理。再次,对经典的柯布-道格拉斯模型进行了修改,将高等教育因素纳入到模型中,提出了一类改进的教育进步贡献率测算模型。在搜集整理的我国2000-2014年的高等教育统计数据基础上,使用新模型进行了测算,得出了高等教育对中西部十九省对经济增长贡献率。最后,在以上实证分析的基础上,结合测算结果对中西部省份高等教育存在的问题,提出了一些切合实际的建议。
[Abstract]:In recent years, the bottlenecks and problems encountered in China's economic development show that in addition to the increase in the investment of production factors such as capital and labor, the improvement of the use efficiency of production factors and the increase of labor productivity can promote the sustained economic growth by relying on technological progress. Meanwhile, the theory of human capital shows that education can promote the progress of technology on the one hand. Step, on the other hand, it can bring the accumulation of human capital, so developing education, especially the highest education at the highest level of education, is the only way to boost the progress of science and technology and lead the transformation and upgrading of the economic structure. The achievements of China's higher education have been affirmed at the same time to find the still existing problems and disadvantages. Secondly, a kind of improved fuzzy soft clustering algorithm is designed. The new algorithm combines the genetic algorithm, the central gravitation algorithm and the fuzzy clustering algorithm together, the genetic algorithm adaptively determines the number of classifications, the central gravitational algorithm is superior. A fuzzy clustering algorithm is used to determine the membership degree of sample data in each class. The new algorithm can adaptively determine the number of sample data, thus realizing unsupervised classification. The economic data of the nineteen provinces in the central and western regions are taken as samples and the nineteen provinces in the middle and western regions are divided into three categories by the improved fuzzy soft clustering algorithm. The ratio of contribution ratio of higher education in the same province is more scientific and reasonable. Again, the classic Cobb Douglas model is revised, the factors of higher education are incorporated into the model, and a kind of improved calculation model of the contribution rate of education progress is put forward. On the basis of the collection and collation of 2000-2014 years of Higher Education Statistics in China, By using the new model, the contribution rate of higher education to the economic growth of the nineteen provinces in the central and western regions is obtained. Finally, on the basis of the above empirical analysis, some practical suggestions are put forward on the problems of higher education in the central and western provinces.
【学位授予单位】：西安科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：G649.2

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