Multiscale Convolutional Stochastic Configuration Network Soft Sensor Modeling Method.

Abstract

To address the challenges of industrial process modeling caused by multiscale spatiotemporal coupling, a soft sensor method based on the multiscale convolutional stochastic configuration network (MSC-SCN) is proposed. This method introduces a multiscale convolutional strategy into the SCN framework and designs parallel multiscale feature extractors with incremental learning capability under a supervised learning mechanism. Subsequently, cross-scale feature fusion is employed to integrate the multiscale feature maps generated by the previously constructed feature extractors. The output weights of the SCN are then optimized by combining low-rank matrix approximation and regularization methods to improve the efficiency and stability of the inverse of the hidden layer matrix. Experimental comparisons with state-of-the-art methods on three industrial soft sensor tasks demonstrate that the proposed approach yields the best performance and demonstrates high adaptability to multiscale spatiotemporal coupling.