On-Line StatePrediction Of Engines Based On Fast Neural Network Shanghai Jiaotong University

Format:

Book

Conference/Event

Author/Creator:

Gang, Xi, author.

Conference Name:

SAE 2001 World Congress (2001-03-05 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2001

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2001-01-0562

Access Restriction:

Restricted for use by site license