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
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;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.616110