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 :
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