广西科学院学报
廣西科學院學報
엄서과학원학보
JOURNAL OF GUANGXI ACADEMY OF SCIENCES
2012年
1期
4-6
,共3页
物流配送中心%选址%粒子群算法%BP神经网络%IPSO-BP神经网络
物流配送中心%選阯%粒子群算法%BP神經網絡%IPSO-BP神經網絡
물류배송중심%선지%입자군산법%BP신경망락%IPSO-BP신경망락
distribution center%location selection%PSO algorithm%BP neural network%IPSO-BP neural network
改进标准粒子群优化算法（PSO）的惯性权重参数,提出基于IPSO的BP神经网络算法,以提高物流配送中心选址的预测精度。仿真结果表明,IPSO-BP神经网络算法的预测精度优于常规BP神经网络算法,不仅改进了网络的收敛速度并且提高了预测准确性。
改進標準粒子群優化算法（PSO）的慣性權重參數,提齣基于IPSO的BP神經網絡算法,以提高物流配送中心選阯的預測精度。倣真結果錶明,IPSO-BP神經網絡算法的預測精度優于常規BP神經網絡算法,不僅改進瞭網絡的收斂速度併且提高瞭預測準確性。
개진표준입자군우화산법（PSO）적관성권중삼수,제출기우IPSO적BP신경망락산법,이제고물류배송중심선지적예측정도。방진결과표명,IPSO-BP신경망락산법적예측정도우우상규BP신경망락산법,불부개진료망락적수렴속도병차제고료예측준학성。
The BP neural network based on the improved particles warm optimization（IPSO） was proposed in this paper to improve the prediction accuracy of the distribution center location selection.The simulation results shown that prediction accuracy of the IPSO-BP neural network algorithm was better than that of conventional BP neural network algorithm.IPSO-BP neural network algorithm improved not only the convergence speed of the network but also the prediction accuracy.
