Localized and adaptive soft sensor based on an extreme learning machine with automated self‐correction strategies

Abstract

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.

Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods

“…The most common types of ANN are multi-layer perceptron (MLP) and radial basis function networks (RBFN). The literature has shown that ANN is especially suitable for implementation of soft sensors, and these have indeed been used [38,39,40,41,42,43,44,45,46,47]. More recently, deep learning has been used to create soft sensors also successfully [48,49,50,51].…”