The nature of statistical learning theory
The nature of statistical learning theory
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Support vector density estimation
Advances in kernel methods
Large Scale Kernel Regression via Linear Programming
Machine Learning
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and Symmetries
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A tutorial on support vector regression
Statistics and Computing
Information Sciences: an International Journal
A trainable feature extractor for handwritten digit recognition
Pattern Recognition
Incorporating prior knowledge in support vector regression
Machine Learning
Neural Control of Fast Nonlinear Systems— Application to a Turbocharged SI Engine With VCT
IEEE Transactions on Neural Networks
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Information Sciences: an International Journal
A comparative study of the multi-objective optimization algorithms for coal-fired boilers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Probabilistic support vector machines for classification of noise affected data
Information Sciences: an International Journal
Information Sciences: an International Journal
Optimal control location for the customer-oriented design of smart phones
Information Sciences: an International Journal
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This paper considers nonlinear modeling based on a limited amount of experimental data and a simulator built from prior knowledge. The problem of how to best incorporate the data provided by the simulator, possibly biased, into the learning of the model is addressed. This problem, although particular, is very representative of numerous situations met in engine control, and more generally in engineering, where complex models, more or less accurate, exist and where the experimental data which can be used for calibration are difficult or expensive to obtain. The first proposed method constrains the function to fit to the values given by the simulator with a certain accuracy, allowing to take the bias of the simulator into account. The second method constrains the derivatives of the model to fit to the derivatives of a prior model previously estimated on the simulation data. The combination of these two forms of prior knowledge is also possible and considered. These approaches are implemented in the linear programming support vector regression (LP-SVR) framework by the addition, to the optimization problem, of constraints, which are linear with respect to the parameters. Tests are then performed on an engine control application, namely, the estimation of the in-cylinder residual gas fraction in Spark Ignition (SI) engine with Variable Camshaft Timing (VCT). Promising results are obtained on this application. The experiments have also shown the importance of adding potential support vectors in the model when using Gaussian RBF kernels with very few training samples.