NDLI: Local Linear Models adaptation for a 4 Inj — 2PP common-rail pressure system

Content Provider	IEEE Xplore Digital Library
Author	Ioanas, G.L. Dragomir, T.L.
Copyright Year	2013
Description	Author affiliation: Powertrain Engine Syst., Continental Automotive Timisoara, Timisoara, Romania (Ioanas, G.L.) \|\| Fac. of Autom. & Comput. Sci., “Politeh.” Univ. of Timisoara, Timişoara, Romania (Dragomir, T.L.)
Abstract	The implementation of a Neuro-Fuzzy nonlinear adaptive structure, with Local Linear Models (LLM), designed for fuel pressure estimation in diesel common-rail (CR) hydraulic system, represents the main topic. Hydraulic systems, in general, are nonlinear and engineers have often struggled to find the best solution to approximate the input-output dependencies. Powerful tools are necessary for splitting the input space in smaller pieces where linear approximations can be considered satisfactory. Neuro-Fuzzy networks, combined with LLM represent the best solution in this case. Using appropriate numerical models, these architectures can be implemented in a real-time environment designed for on-line adaptation of the linear models parameters. The paper demonstrates that the LLMs, and hence, the whole dynamic models parameters of the CR's NeuroFuzzy developed architecture, can be adapted in an on-line environment. The practical results are favorable.
Starting Page	253
Ending Page	258
File Size	1044589
Page Count	6
File Format	PDF
ISBN	9781479903030
e-ISBN	9781479903054
DOI	10.1109/SISY.2013.6662581
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-26
Publisher Place	Serbia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Computational modeling local linear Predictive models system model pressure Fuels Rails common-rail adaptive Computer architecture Mathematical model Neuro-Fuzzy
Content Type	Text
Resource Type	Article

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