Adapted Receptive Field Temporal Convolutional Networks with Bar-Shaped Structures Tailored to Industrial Process Operation Models
Authors:
- 13 March 2020
- journal article
- research article
- Published by American Chemical Society (ACS) in Industrial & Engineering Chemistry Research
- Vol. 59 (13) , 5482-5490
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (11972070)
This publication has 30 references indexed in Scilit:
- Anomaly detection in streaming environmental sensor data: A data-driven modeling approachEnvironmental Modelling & Software, 2010
- Malachite green adsorption by rattan sawdust: Isotherm, kinetic and mechanism modelingJournal of Hazardous Materials, 2008
- Mechanism and modeling of the formation of gaseous alkali sulfatesCombustion and Flame, 2005
- Hybrid neural network modeling of a full‐scale industrial wastewater treatment processBiotechnology and Bioengineering, 2002
- Face recognition: a convolutional neural-network approachIEEE Transactions on Neural Networks, 1997
- Detailed Mechanism and Modeling of Soot Particle Formation1994
- A plant-wide industrial process control problemComputers & Chemical Engineering, 1993
- Data-driven discovery of quantitative rules in relational databasesIEEE Transactions on Knowledge and Data Engineering, 1993
- A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksNeural Computation, 1989
- Learning and optimization of machining operations using computing abilities of neural networksIEEE Transactions on Systems, Man, and Cybernetics, 1989