A novel, nonlinear soft sensor based on a localized, adaptive single‐layer feedforward neural network with random hidden layer weights, also called an extreme learning machine, combined with the recursive partial least squares algorithm to update the linear output layer weights, is explored. The soft sensor is highly adaptive with minimal operator input, and automated mechanisms are included to self‐correct numerous aspects of the underlying model. For instance, mechanisms are put in place to automatically select an optimized local model region describing the current process dynamics from the historical data when the current prediction error reaches an adaptively computed threshold. Additionally, the new soft sensor simultaneously employs an ensemble of models with diverse recursive partial least squares forgetting factors with automated and adaptive reweighting of the models in the ensemble, thus enabling real‐time model memory adjustment. The validity of the method is shown by comparison with numerous other soft sensor methods for the prediction of the activity of a polymerization catalyst.

中文翻译：

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基于具有自动自校正策略的极限学习机的局部自适应软传感器

探索了一种基于具有随机隐藏层权重的局部自适应单层前馈神经网络（也称为极限学习机）的新型非线性软传感器，结合递归偏最小二乘算法来更新线性输出层权重。软传感器具有高度自适应性，操作员输入最少，并且包含自动机制以自我校正基础模型的许多方面。例如，当当前预测误差达到自适应计算的阈值时，建立机制以从历史数据中自动选择描述当前过程动态的优化局部模型区域。此外，新的软传感器同时采用具有不同递归偏最小二乘遗忘因子的模型集合，对集合中的模型进行自动和自适应重新加权，从而实现实时模型记忆调整。该方法的有效性通过与许多其他用于预测聚合催化剂活性的软传感器方法进行比较来证明。