Fuzzy-Constrained Incremental Random Weight Network for Industrial Process Soft Sensing

Data-driven modeling has emerged as a powerful approach for soft sensing of key performance indices in industrial processes. This approach can leverage the collected process data to establish precise soft sensors without requiring explicit physical knowledge. To cope with uncertainties and nonlinear...

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Bibliographic Details
Published in:	IEEE sensors journal Vol. 25; no. 20; pp. 38154 - 38167
Main Authors:	Wang, Qianjin, Luo, Yunfeng, Dai, Wei, Ma, Xiaoping
Format:	Journal Article
Language:	English
Published:	New York IEEE 15.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptation models Artificial neural networks Chaos theory Clustering Compact constraint Constraints Data mining Data processing Data-driven modeling Datasets Dynamical systems Fuzzy logic Fuzzy neural networks Greville’s method incremental random weight network (RWN) industrial process soft sensing Intelligent sensors Modelling Nonlinear dynamics Nonlinearity Optimization Performance evaluation Performance indices Soft sensors Subsystems System identification Takagi-Sugeno model Takagi–Sugeno (T–S) fuzzy system Training
ISSN:	1530-437X, 1558-1748
Online Access:	Get full text
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