Browse Publications Technical Papers 2001-01-0562
2001-03-05

On-Line StatePrediction Of Engines Based On Fast Neural Network 2001-01-0562

A flat neural network is designed for the on-line state prediction of engine. To reduce the computational cost of weight matrix, a fast recursive algorithm is derived according to the pseudoinverse formula of a partition matrix. Furthermore, the forgetting factor approach is introduced to improve predictive accuracy and robustness of the model. The experiment results indicate that the improved neural network is of good accuracy and strong robustness in prediction, and can apply for the on-line prediction of nonlinear multi input multi output systems like vehicle engines.

SAE MOBILUS

Subscribers can view annotate, and download all of SAE's content. Learn More »

Access SAE MOBILUS »

Preview Document Add to Cart
Members save up to 16% off list price.
Login to see discount.
Special Offer: Download multiple Technical Papers each year? TechSelect is a cost-effective subscription option to select and download 12-100 full-text Technical Papers per year. Find more information here.
We also recommend:
TECHNICAL PAPER

A Neural Model of Friction Material Behaviour

2006-01-3200

View Details

TECHNICAL PAPER

Development of a Fall Detection Algorithm for Powered Two Wheelers Application

2014-32-0022

View Details

TECHNICAL PAPER

An Electronic Throttle Simulation Model with Automatic Parameter Tuning

2005-01-1441

View Details

X