DocumentCode
3284339
Title
An modified gradient training algorithm of process neural network
Author
Fan, Yang
Author_Institution
Wuhan Electr. Power Tech. Coll., Wuhan, China
fYear
2011
fDate
15-17 April 2011
Firstpage
2966
Lastpage
2969
Abstract
Process neural network (PNN) is a new neural network. This paper intends to improve the training speed of the discrete PNN with a Levenberg-Marquardt modified gradient training algorithm. The training steps and the algorithm are illustrated. Further, an experiment for the prediction of the humidity of sealed boxes is taken as a case study. This modified algorithm is employed in the case study where its fast convergence is convinced.
Keywords
learning (artificial intelligence); neural nets; Levenberg-Marquardt modified gradient training algorithm; discrete PNN; process neural network; sealed box humidity; Artificial neural networks; Convergence; Helium; Humidity; Prediction algorithms; Software; Training; Levenberg-Marquardt algorithm; process neural network; training speed;
fLanguage
English
Publisher
ieee
Conference_Titel
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-8036-4
Type
conf
DOI
10.1109/ICEICE.2011.5777811
Filename
5777811
Link To Document