部分最小二乘算法的神经元网络实现  

Neural Network Implementation of the PLS Algorithm

作　　者：赵仕健[1] 徐用懋[1] 

机构地区：[1]清华大学自动化系,北京100084

出　　处：《清华大学学报（自然科学版）》2004年第10期1348-1351,共4页Journal of Tsinghua University(Science and Technology)

基　　金：国家"八六三"高技术项目(2001AA413320)

摘　　要：部分最小二乘(PLS)算法在多元统计过程监控等领域得到了广泛应用。但常用的求解方法需要多次迭代求解残差矩阵,不利于对算法的理论分析和结论的解释。基于PLS算法的优化函数形式,该文提出一种新的PLS优化目标函数及相应简化算法。在此基础上构造了PLS算法与线性神经元网络之间的自然映射,给出了相应的训练算法及其理论分析。仿真结果验证了所提出算法的有效性,表明该算法可直接从原数据矩阵得到相应的成分及回归系数,并易于对其进行解释。The partial least squares algorithm is widely used for multivariate statistical process monitoring among other topics. The popular 'bootstrap' algorithm, however, requires repeated iterative calculations of the residual matrix, which makestheoretical analyses difficult and hinders concise interpretations of the results. This paper presents a simplified algorithm based on an optimization objective function. The partial least squares algorithm can then be easily mapped to a linear neural network. A weight updating strategy is provided with a rigorous mathematical proof. The efficiency of the technique is demonstrated through a simulation which demonstrates that the latent structures and regression coefficients can be directly obtained from the original data matrices and the results can be easily interpreted.

关 键 词：神经元网络 部分最小二乘 简化算法 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]