基于模糊支持向量机的小批量生产质量智能预测方法

在分析比较目前常用的智能工序预测技术及其特点的基础上,提出一种适合小批量生产过程的质量智能预测模型,并给出了相应的预测过程和算法.由于该模型中以模糊支持向量机(FLS-SVM)技术为智能核心,一方面较好的解决小样本学习问题,避免了人工神经网络等智能方法在对小批量生产过程质量进行预测时所表示出的过学习、泛化能力弱等缺点.另一方面,通过隶属度函数对样本进行模糊化,达到样本优化选择,实现历史数据“重近轻远”的预测效果.通过对具体加工过程的预测实验,并与其它几种常见预测方法效果进行对比,说明本文方法实现容易,建模速度快,小样本的泛化能力强,为实现小批量加工过程的在线质量预测与控制提供可行的思路.

Intelligent Prediction Method for Small-Batch Producing Quality based on Fuzzy Least Square SVM

A quality intelligent prediction model for small-batch producing process was proposed in the paper,after comparing with the common used approaches of procedure intelligent prediction and their characteristics.The prediction process and algorithm were presented too.The model takes fuzzy least square support vector machine(FLS-SVM) as the intelligent kernel.On one hand,it can solve the small-batch learning better and avoid the disadvantages,such as over-trainning,weak normization capability,etc.,of artificial neural networks prediction.On the other hand,it makes samples fuzzy by membership function to choose optimum samples and make history data 'farther is more weight'.After doing lots of prediction experiments and comparing with other common prediction methods,the method proposed in the paper proved to be good normization capability,more rapidly built,and more easily realized.It offers feasibility to predict and control small-batch machining process online.