基于粗糙PSO-BP神经网络的冷链物流服务商选择研究

为帮助冷链食品生产企业快速选择最佳冷链物流服务商，在传统BP神经网络基础上融合粗糙集和粒子群算法，构建了粗糙PSO-BP神经网络模型。该模型利用粗糙集剔除原始数据中的冗余信息，使输入指标更加精简；采用粒子群算法代替梯度下降法对神经网络权重进行训练，使输出结果不易陷入局部极小值，增强网络泛化能力。通过算例验证该模型的有效性和可行性。结果表明，该模型在提高运算速度的同时，预测误差为BP神经网络模型的40.94%，预测结果更加准确可靠，为冷链食品生产企业快速选择最佳冷链物流服务商提供一种新的方法指导。

A Study of Cold Chain Logistics Service Provider Selection Based on Rough PSO-BP Neural Network

In order to help cold chain food production enterprises to select the best cold chain logistics service providers quickly, a rough PSO-BP neural network model was constructed by integrating rough set and particle swarm algorithm on the basis of traditional BP neural network. The model uses rough set to eliminate the redundant information in the original data and make the input index more compact. Particle swarm optimization is used instead of gradient descent to train the weights of the neural network, so that the output results are not easily trapped in local minimal and the generalization ability of the network is enhanced. Finally, an example is given to verify the validity and feasibility of the model. The results show that while improving the operation speed, the prediction error of the model is 40.94% of the BP neural network model, and the prediction result is more accurate and reliable, which provides a new method for the cold chain food production enterprises to quickly select the best cold chain logistics service provider.