The hybrid grey-based model for cumulative curve prediction in manufacturing system
The hybrid grey-based model for cumulative curve prediction in manufacturing system
复制标题
DOI：
10.1007/s00170-009-2199-0
复制
发表时间：
2010-03
期刊：
影响因子：
--
通讯作者：
GuoDong Li;S. Masuda;Chen-Hong Wang;D. Yamaguchi;M. Nagai
中科院分区：
文献类型：
--
作者：
GuoDong Li;S. Masuda;Chen-Hong Wang;D. Yamaguchi;M. Nagai
累积曲线预测已被广泛应用于许多制造系统中，以实现对生产过程的有效控制和管理。本文根据灰色系统理论，提出了一种单变量一阶灰色模型GM（1，1），用以解决累积曲线的预测问题。为提高GM（1，1）模型的预测能力，将三次样条函数引入GM（1，1）模型。新生成的模型被定义为3spGM（1，1）。然后，粒子群优化（PSO）算法应用于3spGM（1，1），使预测性能可以进一步提高。我们将最优版本称为P-3spGM（1，1）。最后，提出了一种基于人工神经网络（ANN）的残差补偿方法，以获得最佳的预测性能。生产过程中的累积曲线被用来验证所提出的模型。
The cumulative curve prediction has been widely used in many manufacturing systems to achieve the efficient control and management for production processes. In this paper, the grey model GM(1, 1), a single-variable first-order grey model, which is based on the grey system theory, is proposed to resolve the prediction problem of cumulative curve. To improve the prediction capability of GM(1, 1), the cubic spline function is integrated into GM(1, 1). The newly generated model is defined as 3spGM(1, 1). Then, the particle swarm optimization (PSO) algorithm is applied to 3spGM(1, 1) so that the prediction performance can be further improved. We refer to the optimal version as P-3spGM(1, 1). Finally, a residual compensation approach based on artificial neural network (ANN) is proposed to acquire the best prediction performance. The cumulative curve in the production process is used to validate the proposed models.