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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Vydáno v:	IEEE sensors journal Ročník 25; číslo 20; s. 38154 - 38167
Hlavní autoři:	Wang, Qianjin, Luo, Yunfeng, Dai, Wei, Ma, Xiaoping
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 15.10.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
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