基于非线性模糊支持向量机的知识化制造模式与动态环境匹配分类方法

Classification method of matching knowledgeable manufacturing mode with dynamic environment based on nonlinear fuzzy weight SVM

为了评价企业当前知识化制造模式与动态环境因素的匹配性,为企业的快速响应提供依据,提出了一种考虑模糊输入和不均衡样本的非线性模糊加权支持向量机(NFW-SVM)模型.考虑到实际生产面临的动态环境因素具有模糊性和不确定性,引入三角模糊数对模糊因素进行描述.针对不同匹配类别数据样本的不均衡性,设置了不同的分类惩罚因子,以降低小样本错分的比例.将变异算子和具有收缩因子的动态惯性权重引入到标准粒子群优化算法中,利用改进的粒子群算法对模型参数进行优化,提高模型的分类精度.给出了基于NFW-SVM模型的知识化制造模式与动态环境匹配的分类方法.最后,通过实例验证了该方法的有效性和可行性.

To correctly judge the matching category between current knowledgeable manufacturing mode and dynamic environment factors,and provide the basis for rapid response,a model of nonlinear fuzzy weight-support vector machine （NFW-SVM）is proposed in which fuzzy inputs and imbalance of the different matching categories of samples are considered.Considering the vagueness and uncertainty of the dynamic production environment in the actual production,the triangular fuzzy number is adopted to describe the vague factor.For the imbalance characters of the data sample in different categories,different category penalty factors are set up in the model to reduce the fault proportions of small samples.The mutation operator and dynamic inertia weight with constriction factors are introduced to the standard particle swarm optimization algorithm.To enhance the classification accuracy,the model parameters are optimized by the improved particle swarm optimization algo-rithm.The classification method based on NFW-SVM to judge the matching category between dynamic environment factors and current manufacturing mode is presented.Finally,the effectiveness and feasibility of the proposed method are verified by an example.