网络化制造资源状态估计研究——基于主成分分析（PCA）和加权支持向量机（WSVM）

针对网络化制造资源配置受多因素影响，变化趋势复杂，难以用单一预测方法进行有效预测的问题，提出一种新的基于主成分分析和加权支持向量机的智能混合预测模型。模型首先使用主成分分析弱化数据序列波动性，然后借助加权支持向量机在处理小样本和模糊神经系统处理非线性模糊信息的优点，进行趋势预测。研究结果表明，所提出的模型能有效地提高状态估计方案的可行性，为网络化制造资源状态估计的在线实施提供了方便。

Research of Resource Mode Estimation for Networked Manufacturing

Due to the fluctuation and complexity of the allocation of networked manufacturing resources affected by various factors,it is difficult to use a single prediction method to accurately describe its moving trend.So a new hybrid prediction model based on PCA and weighted support vector machine(WSVM) is proposed.In this model,the fluctuation of the data sequence is weakened by the principal component analysis,the WSVM can deal with small samples and neuro fuzzy system is capable of processing non-linear fuzzy information.Testing results show that the proposed method can improve the feasibility of the program in optimal configuration,and it is suitable for on-line resource mode estimation control for networked manufacturing.