Software Sensor Design Using Bayesian Automatic Classification and Back-Propagation Neural Networks

1998 ◽  
Vol 37 (10) ◽  
pp. 3985-3991 ◽  
Author(s):  
F. Z. Chen ◽  
X. Z. Wang
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Automatic Classification ◽  
Sensor Design ◽  
Software Sensor ◽  
Back Propagation Neural Networks

A Reverse Approach in Optimizing Pass Parameters

2010 ◽  
Vol 113-116 ◽  
pp. 1707-1711
Author(s):  
Jian Hua Hu ◽  
Yuan Hua Shuang
Keyword(s):  
Neural Networks ◽  
Finite Element Method ◽  
Finite Element ◽  
Learning Process ◽  
Back Propagation ◽  
Fem Simulation ◽  
Steel Pipe ◽  
Good Convergence ◽  
Back Propagation Neural Networks ◽  
Element Method

A method combines a back propagation neural networks (BPNN) with the data obtained using finite element method (FEM) is introduced in this paper as an approach to solve reverse problems. This paper presents the feasibility of this approach. FEM results are used to train the BPNN. Inputs of the network are associated with dimension deviation values of the steel pipe, and outputs correspond to its pass parameters. Training of the network ensures low error and good convergence of the learning process. At last, a group of optimal pass parameters are obtained, and reliability and accuracy of the parameters are verified by FEM simulation.

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