DocumentCode :
315186
Title :
Neural network isolation of system inputs for transient modelling and control
Author :
Tascillo, PhD Anya
Author_Institution :
Res. Lab., Ford Motor Co., Dearborn, MI, USA
Volume :
2
fYear :
1997
fDate :
9-12 Jun 1997
Firstpage :
718
Abstract :
A neural network is used to predict the sensitivity of a complex nonlinear system (such as an automobile) to input variation, which will aid greatly in the effort to model the system and the effects of changes to its controllers. A blend of signal processing techniques is used to provide maximum resolution neural network inputs for various drivers, vehicles, engine technologies, transmissions, velocity traces, and operating temperatures. The neural net predicts what four different vehicle outputs will be, given a sample of driving inputs
Keywords :
automobiles; large-scale systems; mechanical engineering computing; neural nets; nonlinear systems; sensitivity analysis; signal processing; transient response; automobile; complex nonlinear system; input isolation; neural network; sensitivity analysis; signal processing; transient modelling; Automobiles; Engines; Neural networks; Nonlinear control systems; Nonlinear systems; Predictive models; Signal processing; Signal resolution; Temperature; Vehicle driving;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
Type :
conf
DOI :
10.1109/ICNN.1997.616110
Filename :
616110
Link To Document :
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