Abstract:
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 the partition matrix. Furthermore the
forgettin 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.
Key words: Engine
model Neural network On-line prediction Nonlinear system
*
This project
is supported by Ford-China Research and Development Foundation (09415526).
Manuscript received on January 4, 2000; revised manuscript March 15, 2000
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