By Topic

Application of improved DBD algorithm based bp neural network on fault diagnosis for fuel supply system in a certain diesel engine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Feng Fuzhou ; Dept. of Mech. Eng., Acad. of Armored Force Eng., Beijing, China ; Si Aiwei ; Xing Wei

In order to overcome the drawbacks of a neural network based on back propagation (BP) algorithm, such as too slow to converge and easy to be trapped into a local minimum, a new modified algorithm is proposed in this paper, in which the grads information of the network are exchanged dynamically in each iteration step, and the increment factor of learning rate and interaction function in delta-bar-delta (DBD) algorithm are improved based on the idea of cross and mutation in Genetic algorithm (GA). The new algorithm has been applied in the fault diagnosis of a fuel supply system in a certain diesel engine successfully.

Published in:

Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on  (Volume:2 )

Date of Conference:

10-12 June 2011