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Received:September 23, 2019 Revised:October 15, 2019
Received:September 23, 2019 Revised:October 15, 2019
中文摘要: 以实现科学、准确、可操作的区域自主创新能力评估分类为目标，提出一种基于决策树遗传算法和BP神经网络的组合模型（Decision Tree Genetic Algorithm and Back Propagation，DTGA-BP）.利用决策树对评估指标进行特征选择并通过优化隐藏层神经元数目对神经网络的结构进行改进；采用非线性的交叉变异概率值的遗传操作结合一种新的选择算子方式优化BP神经网络的初始权重与阈值.实验结果表明，组合模型的评估结果相比传统的主观赋值法更为科学准确；较单一BP神经网络模型和GA-BP模型在分类精度方面分别提高了41%和20%.
Abstract:Aiming at the scientific, accurate, and operable regional independent innovation capability evaluation classification, a Decision Tree Genetic Algorithm and Back Propagation neural network (DTGA-BP) is proposed. The characteristics of the evaluation index are selected and the structure of the neural network is improved by optimizing the number of neurons in the hidden layer. The genetic operation of the nonlinear crossover probability value is combined with a new selection operator to optimize the initial weight and threshold of the BP neural network. The experimental results show that the evaluation results of the combined model are more scientific and accurate than the traditional subjective valuation method. Compared with the single BP neural network model and the GA-BP model, the classification accuracy is improved by 41% and 20%, respectively.
keywords: regional innovation BP neural network decision tree genetic algorithm classification accuracy
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基金项目:山西省重点研发计划（高新技术领域）（201803D121106）；全国高等学校计算机教育研究会2019年度课题（CERACU2019R02）
引用文本：
李晨阳,刘春霞,党伟超,白尚旺,潘理虎.基于DTGA-BP组合模型的区域创新能力评价.计算机系统应用,2020,29(5):152-158
LI Chen-Yang,LIU Chun-Xia,DANG Wei-Chao,BAI Shang-Wang,PAN Li-Hu.Regional Innovation Capability Evaluation Based on DTGA-BP Combined Model.COMPUTER SYSTEMS APPLICATIONS,2020,29(5):152-158
李晨阳,刘春霞,党伟超,白尚旺,潘理虎.基于DTGA-BP组合模型的区域创新能力评价.计算机系统应用,2020,29(5):152-158
LI Chen-Yang,LIU Chun-Xia,DANG Wei-Chao,BAI Shang-Wang,PAN Li-Hu.Regional Innovation Capability Evaluation Based on DTGA-BP Combined Model.COMPUTER SYSTEMS APPLICATIONS,2020,29(5):152-158
