Estimation of Engine Maps: A Regularized Basis-Function Networks Approach

Autor:	G. Prodi, G. De Nicolao, Carlo Siviero, M. Neve
Rok vydání:	2009
Předmět:	Engineering Artificial neural network business.industry Dynamic data Extrapolation Basis function Regularization (mathematics) Manifold vacuum Control and Systems Engineering Control theory Radial basis function Boundary value problem Electrical and Electronic Engineering business Algorithm
Zdroj:	IEEE Transactions on Control Systems Technology. 17:716-722
ISSN:	1558-0865 1063-6536
DOI:	10.1109/tcst.2008.2002040
Popis:	In this brief, a new methodology for the identification of engine maps from static data is presented. In order to enhance the flexibility of the model and exploit prior knowledge on the boundary conditions of the maps, a basis function neural network with a large number of neurons is used. To ensure smoothness of the estimated map as well as guarantee reliable extrapolation properties, the weights are estimated via a regularization strategy. Dynamic data are used to validate the new methodology. For this purpose, the estimated maps are included in a mean value model whose simulated manifold pressure and crankshaft speed are compared with the experimental ones. The results show a clear improvement with respect to the performances obtained resorting to standard radial basis function networks.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::de899d7e251c41ca84a46b420584e27d https://doi.org/10.1109/tcst.2008.2002040 Zobrazit plný text záznamu