Non-linear projection to latent structures revisited (the neural network PLS algorithm)
Non-linear projection to latent structures revisited (the neural network PLS algorithm)
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DOI：
10.1016/s0098-1354(99)00291-4
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发表时间：
1999-11
影响因子：
4.3
通讯作者：
G. Baffi;E. Martin;A. Morris
中科院分区：
文献类型：
--
作者：
G. Baffi;E. Martin;A. Morris
投影到潜在结构（PLS）已被证明是一个强大的线性回归技术的问题，其中的数据是嘈杂的，高度相关的，只有有限数量的观察。然而，在许多实际情况下，工业数据可能表现出非线性行为。在文献中已经提出了许多方法来在线性PLS框架内集成非线性特征，从而提供非线性PLS算法。本文提出了一种神经网络PLS算法的开发方法，其中S形神经网络或径向基函数（RBF）网络完全集成在PLS算法中，使用PLS输入外模型中的权重更新。在现有的神经网络PLS算法提供的建模能力的潜在改进进行评估，通过比较模拟的pH值中和过程。
Projection to latent structures (PLS) has been shown to be a powerful linear regression technique for problems where the data is noisy and highly correlated and where there are only a limited number of observations. However, in many practical situations, industrial data can exhibit non-linear behaviour. A number of methodologies have been proposed in the literature to integrate non-linear features within the linear PLS framework and thus provide a non-linear PLS algorithm. This paper presents an approach to the development of neural network PLS algorithms where either a sigmoid neural network or a radial basis function (RBF) network is fully integrated within the PLS algorithm using weight updating in the PLS input outer models. The potential improvements in modelling capability provided over the existing neural network PLS algorithms is assessed through comparisons on a simulation of a pH neutralisation process.