考虑替代性的SOM神经网络卷烟配方模块分类方法研究

Research on the classification method of cigarette blend modules with SOM neural network considering alternatives

为了提高模块替代决策效率和整个卷烟制造系统柔性与生产效率,提出了一种基于替代度的SOM神经网络模型对卷烟配方模块进行分类,并与历史替代统计结果进行比对。结果表明,替代度能较好地衡量模块间的替代程度,替代度越大,每个类别中的各项质量指标一致性越强,模块质量越相似,越推荐进行相互替代;在以不同替代度标准取值对卷烟配方模块进行分类时,替代度标准值越大,分类越细,选择替代度标准值为3.06作为卷烟配方模块强替代性的标准进行分类时是最合适的,此时每个类别中卷烟配方模块质量具有较高的相似性。基于替代度的SOM神经网络分类结果显示,发生类内替代的比例明显优于一般SOM神经网络算法、两阶段聚类算法和K-means聚类算法,当替代度标准值为3.06时,类内相互替代率可达95.39%,而类间替代率不足5.00%,相同类别模块替代率良好。

In order to improve the decision-making efficiency of module substitution and the flexibility and production efficiency of the entire cigarette manufacturing system,a substitution degree based SOM neural network model was proposed to classify cigarette blend modules,and the effect of this model was compared with the historical substitution statistical results.The results showed that the sub⁃stitution degree could better measure the degree of substitution between modules.The larger the substitution degree,the stronger the consistency of the quality indicators in each category,the more similar the quality of the modules,and the more recommended for mu⁃tual substitution.When classifying cigarette formula modules with different substitution degree standard values,the larger the value was,the finer the classification was.It was most appropriate to select the substitution degree standard value of 3.06 as the standard of strong substitution of cigarette formula modules for classification where the quality of cigarette blend modules in each category had a high similarity.The classification results of SOM neural networks based on substitution degree showed that the proportion of intra-class substitution was superior to general SOM neural network algorithms,two-stage clustering algorithms,and K-means clustering algo⁃rithms.When the substitution degree standard value was 3.06,the intra-class mutual substitution rate could reach 95.39%,while the inter-class substitution rate was less than 5.00%.The replacement rate of modules in the same class was excellent.