Artificial neural networks in fault diagnosis and control
- 28 February 1994
- journal article
- Published by Elsevier in Control Engineering Practice
- Vol. 2 (1) , 89-101
- https://doi.org/10.1016/0967-0661(94)90577-0
Abstract
No abstract availableKeywords
This publication has 18 references indexed in Scilit:
- Orthogonal least squares learning algorithm for radial basis function networksIEEE Transactions on Neural Networks, 1991
- Diagnosis using backpropagation neural networks—analysis and criticismComputers & Chemical Engineering, 1990
- Practical identification of NARMAX models using radial basis functionsInternational Journal of Control, 1990
- Use of neural nets for dynamic modeling and control of chemical process systemsComputers & Chemical Engineering, 1990
- Fault diagnosis in dynamic systems using analytical and knowledge-based redundancyAutomatica, 1990
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989
- Artificial neural network models of knowledge representation in chemical engineeringComputers & Chemical Engineering, 1988
- Malfunction diagnosis using quantitative models with non‐boolean reasoning in expert systemsAIChE Journal, 1987
- Internal Model Control: extension to nonlinear systemIndustrial & Engineering Chemistry Process Design and Development, 1986
- Process fault detection based on modeling and estimation methods—A surveyAutomatica, 1984