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Neuro-Hybrid Models for Automotive System Identification
Authors: Ventura Assuncao
Abstract:In automotive systems almost all steps concerning the calibration of several control systems, e.g., low idle governor or boost pressure governor, are made with the vehicle because the timeto- production and cost requirements on the projects do not allow for the vehicle analysis necessary to build reliable models. Here is presented a procedure using parametric and NN (neural network) models that enables the generation of vehicle system models based on normal ECU engine control unit) vehicle measurements. These models are locally valid and permit pre and follow-up calibrations so that, only the final calibrations have to be done with the vehicle.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1075653Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1186
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