Abstract Actual industrial data inevitably contain a variety of outliers for various reasons. Even a single outlier may have a large distortion effect on modeling performance with conventional algorithms, not to mention the complicated process modeling by the imperfect industrial data existing various outliers both in input direction and output direction. Therefore, the robustness of the algorithm must be fully considered in modeling of complicated industrial processes. Aiming at this, the robust neural network with random weights based on generalized M-estimation and PLS (GM-R-NNRW) is proposed for data modeling of complicated industrial process, whose samples coexist input and output outliers and have multicollinearity problem. Firstly, the input weights and biases of the proposed GM-R-NNRW are randomly assigned within their respective given ranges. Secondly, the GM-R-NNRW determines the weights of the sample by the residual size of the model and the distance information of the input vector in the high-dimensional space according to the generalized M-estimation. Then these weights were combined to determine the final model contribution of each sample, solving the problem that the samples exist both the input direction and the output direction outliers. Moreover, the improved PLS is used to solve the multicollinearity problem existing in data samples. Finally, both data experiment and actual industrial application have showed that the general approximation performance of the algorithm is greatly improved, and an easy-to-use model with better accuracy and robust performance can be obtained.

中文翻译：

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基于广义 M 估计和 PLS 的具有随机权重的鲁棒神经网络，用于不完美的工业数据建模

摘要 实际工业数据由于各种原因不可避免地包含各种异常值。即使是单个异常值也可能对传统算法的建模性能产生很大的失真影响，更不用说不完善的工业数据在输入方向和输出方向都存在各种异常值所造成的复杂过程建模。因此，在复杂工业过程建模时必须充分考虑算法的鲁棒性。针对这一点，提出了基于广义M估计和PLS的随机权重鲁棒神经网络（GM-R-NNRW），用于复杂工业过程的数据建模，其样本共存输入和输出异常值并存在多重共线性问题。首先，提议的 GM-R-NNRW 的输入权重和偏差在各自给定的范围内随机分配。其次，GM-R-NNRW根据模型的残差大小和输入向量在高维空间中的距离信息，根据广义M估计确定样本的权重。然后结合这些权重来确定每个样本的最终模型贡献，解决样本在输入方向和输出方向都存在异常值的问题。此外，改进的PLS用于解决数据样本中存在的多重共线性问题。最后，无论是数据实验还是实际工业应用都表明，该算法的一般逼近性能有了很大的提高，