DocumentCode
2448416
Title
Application of Recursive Predict Error Neural Networks in Mechanical Propertise Forecasting
Author
Wang Wu ; Zhang Yuan-min
Author_Institution
Electro-Inf. Coll., Xuchang Univ., Xuchang, China
fYear
2009
fDate
25-26 April 2009
Firstpage
132
Lastpage
135
Abstract
Parameters control problem was crucial in rolling industrial, but the mechanical properties forecasting of strip steel was an information space incompletely and non-linear complex system which was hard for traditional method. Artificial neural networks was a non-linear system with strong non-linear modeling ability, but the traditional BP neural networks has many shortcomings like easily step into local minimum, with weak generalization ability and the middle layer neuron are hard to determine, so the artificial neural networks with recursive predict error (RPE) algorithm was proposed in this paper with the networkspsila structure, algorithm, sample data selection also presented, the simulation shows its effective and can successfully applied into parameters control of rolling industrial.
Keywords
backpropagation; mechanical properties; neural nets; production engineering computing; rolling; sheet metal processing; BP neural network; artificial neural network; information space; mechanical properties forecasting; mechanical propertise forecasting; middle layer neuron; nonlinear complex system; nonlinear system; parameters control problem; recursive predict error neural network; rolling industrial; strip steel; strong nonlinear modeling; weak generalization ability; Aerospace industry; Artificial neural networks; Control systems; Electrical equipment industry; Industrial control; Mechanical factors; Metals industry; Neural networks; Nonlinear control systems; Predictive models; Neural networks; Recursive predict error(RPE) algorithm; mechanical propertise; simulation;
fLanguage
English
Publisher
ieee
Conference_Titel
Artificial Intelligence, 2009. JCAI '09. International Joint Conference on
Conference_Location
Hainan Island
Print_ISBN
978-0-7695-3615-6
Type
conf
DOI
10.1109/JCAI.2009.30
Filename
5158957
Link To Document