An Applied Comparison of the Prediction Intervals of Common Empirical Modeling Strategies
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Summary
The focus of this work is to provide a direct comparison of point-wise confidence intervals for each of the three methodologies for signal validation and calibration verification, using 3 different nonlinear modeling paradigms: Artificial Neural Networks, Neural Network Partial Least Squares, and Kernel Regression.
- Type
- article
- Published
- 2003-01-01
- Cited by
- 4
- References
- 35
- OpenAlex
- https://openalex.org/W38890282
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:6858148
Keywords
Econometrics, Statistics, Computer science, Environmental science, Mathematics
References
- Instrument Surveillance and Calibration Verification: A Case Study Using Two Empirical Modeling Paradigms
- Confidence estimation methods for neural networks : a practical comparison
- Nonlinear partial least squares
- Use of Autoassociative Neural Networks for Signal Validation
- A Novel Approach to Process Modeling for Instrument Surveillance and Calibration Verification
- Application of Neural Networks for Sensor Validation and Plant Monitoring
- Locally Weighted Learning
- Multivariate Locally Weighted Least Squares Regression
- Prediction intervals for non-linear projection to latent structures regression models
- Non-linear projection to latent structures revisited (the neural network PLS algorithm)
- Stochastic regularization of feedwater flow rate evaluation for the venturi meter fouling problem in nuclear power plants
- Nonlinear PLS Modeling Using Neural Networks
- THE USE OF NON LINEAR PARTIAL LEAST SQUARE METHODS FOR ON-LINE PROCESS MONITORING AS AN ALTERNATIVE TO ARTIFICIAL NEURAL NETWORKS
- Prediction Intervals for Artificial Neural Networks
- Confidence bounds for neural network representations
- PLS/neural networks
- A pattern recognition-artificial neural networks based model for signal validation in nuclear power plants
- Nonlinear FIR Modeling via a Neural Net PLS Approach
- An Algorithm for Least-Squares Estimation of Nonlinear Parameters
- Confidence intervals for neural network based short-term load forecasting
Cited by
- Lessons learned from the U.S. nuclear power Plant on-line monitoring programs
- Characterization of used nuclear fuel with multivariate analysis for process monitoring
- A novel fault detection system taking into account uncertainties in the reconstructed signals
- A FRAMEWORK FOR ASSISTING LEARNERS BY INCORPORATING KNOWLEDGE TO AID IN PREDICTING NERVE GUIDANCE CONDUIT PERFORMANCE by William
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