@article{(Open Science Index):https://publications.waset.org/pdf/10651,
	  title     = {Neuro-Hybrid Models for Automotive System Identification},
	  author    = {Ventura Assuncao},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Mechanical and Mechatronics Engineering},
	  volume    = {1},
	  number    = {12},
	  year      = {2007},
	  pages     = {760 - 763},
	  ee        = {https://publications.waset.org/pdf/10651},
	  url   	= {https://publications.waset.org/vol/12},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 12, 2007},
	}