摘要
传统物流中心选址模型存在种种局限,本文通过综合主成分分析和神经网络方法降低神经网络规模,提高神经网络的泛化能力,在解决非线性系统的物流中心选址问题中显示了较高的精度和效率。
Traditional logistics center location has some limitation. This paper proposes a new method-the combination of principal component analysis and the neural network, which can reduce the neural network scale, enhance its generalization. Its demonstrats the high ＂precision and efficiency in the solution of nonlinear programming on logistics center location.
出处
《物流科技》
2006年第7期33-35,共3页
Logistics Sci-Tech
关键词
物流选址
主成分分析
神经网络
logistics centers location
principal component analysis
neural network
