Inventory Management Optimization of Green Supply Chain Using IPSO-BPNN Algorithm under the Artificial Intelligence

This exploration is aimed at reducing the waste of resources in supply chain inventory management and provide better services for green management. It mainly proposes a backpropagation neural network (BPNN) model based on improved particle swarm optimization (IPSO) (IPSO-BPNN) applies it to prediction. First, important technologies intelligent are analyzed from perspective ecological environment. Next, (PSO) algorithm optimized adaptive improvement learning factor addition speed mutation operator. Then, applied training BPNN. Finally, simulation experiment combination conducted. The application fields analyzed. results show that single BPNN will produce large errors process. final error using traditional PSO 0.0259, while by IPSO 0.0163. has higher accuracy, performance, lowest rate. classification rate its set test 1.51 2.16, respectively. mean square 0.0163 0.0229. Under 6 ~ 12 different hidden nodes, daily measurement monthly both low when number nodes 11. Moreover, always than set. structure determined as 6-11-1. prediction module purchase volume suggestions feasible direction development