基于FOA优化BP神经网络的电子商务网站评价研究

Evaluation Research on E-commerce Website Based on FOA-BP Neural Network

为避免传统BP神经网络进行电子商务网站评价存在泛化能力弱、局部最优问题和对评价样本量需求量大的缺点,利用果蝇优化算法的全局寻优能力,提出运用FOA优化BP神经网络的权值和阈值进行电子商务网站评价。结合电子商务网站的特性和各种指标属性,将电子商务评价的20个二级指标作为BP神经网络的输入,在专家评分法的基础上,将专家评分值作为BP神经网络的输出,构建出基于FOA优化BP神经网络的电子商务网站竞争力指数评价的数学模型。选择淘宝网、京东商城、苏宁易购、唯品会等100个电子商务网站在2015年3月~2005年6月的电子商务网站流通数据为研究对象。实验结果表明,在评价效果和评价误差上,FOA-BP算法评价效果明显优于BP神经网络算法,具有评价精度高和误差低的优点,从而验证了FOA-BP进行电子商务网站竞争力指数评价的有效性和可靠性。

To avoid e-commerce website evaluation based on the traditional BP neural network has the shortcomings of weak generalization, local optimal problems and large demand of the evaluation of sample size, using global optimization capability of Fruit Flying optimization algorithm, it is proposed to use FOA to optimize the weights and thresholds for BP neu- ral network and evaluate e-commerce website. Based on characteristics of e-commerce website and properties of various indi- cators, 20 secondary indicators for e-commerce evaluation are taken as the input of BP neural network, based on expert evalu- ation method, the expert evaluation value are taken as output of BP neural network, then based on FOA BP neural network optimization mathematical model of e-commerce sites Competitiveness Index Evaluation is constructed. The circulation data for 100 e-commerce sites such as Taobao, Jingdong Mall, Suning Tesco, VIP from March 2015 to June 2005 are chosen as study object. In view of the evaluation effectiveness and evaluation error, the experimental results show that FOA-BP algo- rithm is better than BP neural network algorithm, with the advantages of high evaluation precision and low evaluation error, which proves the validity and reliability of FOA-BP for e-commerce sites Competitiveness Index Evaluation.