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基于广义神经网络的科技进步指标体系评价研究

发表时间:2013-05-31  浏览量:1091  下载量:447
全部作者: 刘琎聪,易树平,李俊峰,熊世权
作者单位: 重庆大学机械工程学院
摘 要: 针对当前科技进步评价方法的不足,提出运用索洛余值法计算科技进步贡献率,进而采用广义神经网络(general regression neural network, GRNN)进行科技进步指标体系综合评价。以重庆市区县科技进步为例,基于统计数据,采用GRNN评价方法拟合了统计指标与科技进步贡献率之间的关系,比较预测结果和实际结果,评价了重庆市区县科技进步评价体系的有效性。
关 键 词: 管理理论;科技进步;索洛余值法;广义神经网络;评价指标体系
Title: Research on evaluation index system of technological advancement based on GRNN
Author: LIU Jincong, YI Shuping, LI Junfeng, XIONG Shiquan
Organization: College of Mechanical Engineering, Chongqing University
Abstract: According to the deficiency of technological advancement evaluation, we proposed a new model which adopted Solow method of remainder to calculate the contribution rate of technological advancement and employed the general regression neural network (GRNN) to evaluate the index system of technological advancement. We denoted a case study of districts and counties of Chongqing. Based on the statistical data, we employed GRNN to fit relational grade on statistical data and contribution rate. Moreover, with analyzing the predictive value and actual value, we evaluated the effectiveness of index system of technological advancement of districts and counties of Chongqing.
Key words: management theory; technological advancement; Solow method of remainder; general regression neural network; evaluation index system
发表期数: 2013年5月第10期
引用格式: 刘琎聪,易树平,李俊峰,等. 基于广义神经网络的科技进步指标体系评价研究[J]. 中国科技论文在线精品论文,2013,6(10):897-902.
 
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