A new method of soft computing to estimate the contribution rate of S&T progress on economic growth

作者：He, Yong^*; Liao, Nuo; Liu, Hongwei; Zhong, Yinghong

来源：Applied Soft Computing, 2012, 12(6): 1801-1809.

DOI：10.1016/j.asoc.2012.01.007

• Economic contribution rate of S&T progress (ECRST)
• Genetic iterative self-organizing data analysis technique algorithm (GA-ISODATA)
• Fuzzy artificial neural network (FANN)
• Science and technology (S&T) progress

In this paper, soft computing is applied to estimate the contribution rate of science and technology (S&T) progress on economic growth. First, the main influence factors of economic growth are defined, consisting of capital assets, labor force, human capital and research and development (R&D), and the human capital is calculated by improved labor-payment method. Second, target system is categorized by genetic iterative self-organizing data analysis technique algorithm (GA-ISODATA). Then, we set up the I/O model by fuzzy artificial neural network (FANN), with the capital assets, labor force, human capital and R&D as input variables, and the corresponding gross domestic product (GDP) as the output, to extract several fuzzy rules. Last, from the obtained fuzzy rules, we can get the effect of influence factors on economic growth, and calculate the economic contribution rate of S&T progress (ECRST). Take Guangdong province of China as an example, the result indicates that: during the year 2000-2008, Guangdong province (contains 21 cities) could be classified into three clusters according to the S&T progress. The first cluster (High S&T) has an ECRST of 47.52%, and contains 4 cities; the second cluster (Medium S&T) has an ECRST of 42.74%, and contains 4 cities; the third cluster (Low S&T) has an ECRST of 39.96%, and contains 13 cities; the average ECRST of Guangdong province is 44.02%. The result is accordance with the economic reality of Guangdong province, and demonstrates the validity of the proposed method.

• 出版日期2012-6
• 单位广东工业大学