CAJ | 학술논문

针对当前技术创新能力评价方法大多建立在线性模型的基础上，且技术创新能力影响因素较多，可能存在多重共线性的缺陷，本文提出了遗传算法优化的BP神经网络模型。GA－BP神经网络模型在以下几方面做出了改进：①利用了神经网络强大的非线性关系映射能力，避免了传统线性模型的缺陷。②利用遗传算法对评价指标进行了降维，去除了多重共线性。③使用遗传算法从全局搜寻BP神经网络权值和阀值向量，优化了BP神经网络模型，避免了BP神经网络由于使用梯度下降算法，容易陷入局部最优解的缺陷。本文最后选取2008～2013年全国31个省市规模以上工业企业技术创新能力124条数据作为训练样本，31条数据作为测试样本，分别测试遗传算法优化的BP神经网络和未优化的BP神经网络，测试结果显示遗传算法优化的BP神经网络模型预测准确率高于未优化的BP神经网络模型。
침대당전기술창신능력평개방법대다건립재선성모형적기출상，차기술창신능력영향인소교다，가능존재다중공선성적결함，본문제출료유전산법우화적BP신경망락모형。GA－BP신경망락모형재이하궤방면주출료개진：①이용료신경망락강대적비선성관계영사능력，피면료전통선성모형적결함。②이용유전산법대평개지표진행료강유，거제료다중공선성。③사용유전산법종전국수심BP신경망락권치화벌치향량，우화료BP신경망락모형，피면료BP신경망락유우사용제도하강산법，용역함입국부최우해적결함。본문최후선취2008～2013년전국31개성시규모이상공업기업기술창신능력124조수거작위훈련양본，31조수거작위측시양본，분별측시유전산법우화적BP신경망락화미우화적BP신경망락，측시결과현시유전산법우화적BP신경망락모형예측준학솔고우미우화적BP신경망락모형。
At present , most technological innovation ability evaluation methods are established on the basis of the linear model , and the factors that affect the technological innovation capability are many , the multicollinearity may exist among variables . According to the above two reasons , the GA-BP neural network model was proposed in this paper . Genetic algorithm （GA） optimized the BP neural net-work model in the following aspects： ①neural network has the strong ability of dealing with nonlinear system . It avoided the disadvantages of the linear model . ②In order to remove the multicollinearity , the genetic algorithm was used to reduce evaluation index dimension . ③BP neural network used gradient descent algorithm that modified weights and thresholds , and it was easy to fall into local optimal solution . Genetic algorithm was introduced to search the BP neural network weights and thresholds in global scope . Finally , the technical innovation data of industrial enterprises above designated size in the 31 provinces , and cities were selected from year 2008 to 2013 , 124 of them are regard as training samples , others as testing samples . Empirical conclusion shows that forecast accuracy of GA -BP neural network is higher than BP neural network .