Combination Forecasting Model for Predicting the Shelf Life of Two-State Materials Based on Support Vector Machine

Li Zhiwei, G. Qi, Liu Shenyang, Li Zhen
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Abstract

A combination forecasting model based on Support Vector Machine (SVM) whose objective is to minimize the structure risk is proposed. The storage failure of two-state materials tends to fail immediately without any recognizable defeats prior to the failure, which increases the difficulty of forecasting, so the combination forecasting model is often used to optimize the prediction effect. The core ideas of previous combination forecasting models such as those based on forecasting error and those based on nonlinear weighted average are finding the optimal weights, but the structure of forecasting model is fixed. In this paper, three single forecasting models, Weibull distribution statistic method, BP neural network prediction method and SPFM (Sliding Polynomial Fitting Method) are chosen in which their forecast mechanisms are completely different. The results of single forecasting methods are used as training set of SVM. By using libsvm toolbox, we can get the nonlinear mapping functions that have the minimum structure risk. At last, a simulation is conducted to verify this model by using the data from Petroleum Center.
基于支持向量机的双态材料保质期组合预测模型
提出了一种以结构风险最小化为目标的基于支持向量机的组合预测模型。由于双态材料的存储失效往往是立即失效,在失效之前没有任何可识别的缺陷,这增加了预测的难度,因此通常采用组合预测模型来优化预测效果。以往基于预测误差的组合预测模型和基于非线性加权平均的组合预测模型的核心思想是寻找最优权重,但预测模型的结构是固定的。本文选择了威布尔分布统计方法、BP神经网络预测方法和SPFM(滑动多项式拟合方法)三种单一预测模型,它们的预测机制完全不同。将单一预测方法的结果作为支持向量机的训练集。利用libsvm工具箱,可以得到结构风险最小的非线性映射函数。最后利用石油中心的数据进行了数值模拟,验证了该模型的正确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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