Institute of Economics, Changchun University of Finance and Economics, Jilin Changchun 130122, China.
Comput Intell Neurosci. 2022 Oct 11;2022:8198453. doi: 10.1155/2022/8198453. eCollection 2022.
In order to further improve regional economic innovation capability and governance level and solve the problems of lack of attention to evaluation indicators in traditional evaluation methods of regional economic innovation capability and easy to be affected by subjective factors, an evaluation model based on neural network algorithm is proposed. Through re-analysis of regional economic innovation capability evaluation indexes, the model defines the most reasonable combination of characteristics by combining information gain characteristic selection strategy and finally builds a scientific evaluation index system. By testing the prediction accuracy of the experimental discovery model and evaluation index, the neural network model improves by 41% compared with the traditional subjective evaluation method, and the accuracy increases by 20% compared with the GA-BP neural network model. The experiment proves the stability and good convergence effect of the evaluation model.
为进一步提高区域经济创新能力和治理水平，解决传统区域经济创新能力评价方法中对评价指标重视程度不够、易受主观因素影响的问题，提出了一种基于神经网络算法的评价模型。通过对区域经济创新能力评价指标的重新分析，该模型结合信息增益特征选择策略，定义了最合理的特征组合，最终构建了科学的评价指标体系。通过对实验发现模型和评价指标的预测精度进行测试，发现神经网络模型比传统主观评价方法提高了 41%，比 GA-BP 神经网络模型提高了 20%。实验证明了评价模型的稳定性和良好的收敛效果。
