Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge
Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge
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DOI：
10.1016/j.cor.2005.05.019
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发表时间：
2007-04-01
影响因子：
4.6
通讯作者：
Lina, Yao-San
中科院分区：
文献类型：
--
作者：
Li, Der-Chiang;Wu, Chih-Sen;Lina, Yao-San
神经网络被广泛用于从所获取的数据中提取管理知识，但是具有足够的真实的数据并不总是可能的。在动态柔性制造系统（FMS）环境的早期阶段，只有少量的数据，这意味着调度知识往往是不可靠的。本研究的目的是利用数据扩充技术，以获得一个小的数据集，以提高机器学习的FMS调度的准确性。本研究提出一种大趋势扩散技术，以估计一个小的数据集的域范围，并产生人工样本的训练修改后的反向传播神经网络（BPNN）。使用的工具是Pythia软件。FMS仿真模型的结果表明，当所提出的方法被应用到一个非常小的数据集时，学习精度可以显着提高。(c)2005爱思唯尔有限公司保留所有权利。
Neural networks are widely utilized to extract management knowledge from acquired data, but having enough real data is not always possible. In the early stages of dynamic flexible manufacturing system (FMS) environments, only a litter data is obtained, and this means that the scheduling knowledge is often unreliable. The purpose of this research is to utilize data expansion techniques for an obtained small data set to improve the accuracy of machine learning for FMS scheduling. This research proposes a mega-trend-diffusion technique to estimate the domain range of a small data set and produce artificial samples for training the modified backpropagation neural network (BPNN). The tool used is the Pythia software. The results of the FMS simulation model indicate that learning accuracy can be significantly improved when the proposed method is applied to a very small data set. (c) 2005 Elsevier Ltd. All rights reserved.