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中文摘要：
针对注塑间歇过程多阶段、缓慢时变、非线性和质量变量测量值不能在线获得等特点，提出子时段滑动窗口广义回归神经网络质量预测方法，首先，采用分类算法对三维数据矩阵的时间片PCA负载矩阵进行分析，根据相关性分析把注塑过程划分为几个子时段，然后确定与重量密切相关的阶段，在确定的阶段内采用滑动窗口建立GRNN多模型，解决常规MPLS在工业应用过程中存在的几个潜在问题：（1）静态单一模型；（2）模型失配问题；（3）MPLS线性方法不能充分有效压缩和抽取非线性过程信息；（4）估计未来测量变量所引进的模型偏差。所提方法与子时段滑动窗口MPLS方法进行仿真比较。结果证明了所提方法的有效性。
英文摘要：
For multistage, time-variant, nonlinear characteristic and the unavailable on-line product qualities of injection molding batch process, a sub-stage moving window generalized regression neural network （GRNN） method was proposed. Using an clustering arithmetic, PCA P-loading matrices of time-slice matrices was clustered according to relevance, and injection molding process was divided into several operation stages, the most relevant stage to the quality variable was defined, and applying moving windows to un-fold stage data according to time, sub-stage GRNN models were developed for every windows for on-line quality prediction. The proposed method easily handles the following problems： （1） static single model; （2） process and its model do not match; （3） linear method may not be efficient in compressing and extracting nonlinear process data; （4） errors are added by estimating the future trajectory of the ongoing batch. For comparison purposes, a sub-MPLS quality model of every moving window was established. The results demonstrate the effectiveness of the proposed method.
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