scholarly journals Using back propagation neural networks for prediction of individual cell performance in a long string lead-acid peak shaving battery

1995 ◽  
Author(s):  
Richard E Young
Keyword(s):  
Neural Networks ◽  
Individual Cell ◽  
Back Propagation ◽  
Cell Performance ◽  
Peak Shaving ◽  
Back Propagation Neural Networks ◽  
Lead Acid

Prediction of individual cell performance in a large lead/acid peak-shaving battery

1993 ◽  
Vol 45 (1) ◽  
pp. 73-92 ◽  
Author(s):  
Sam P. Perone ◽  
Pei-Hwei Chen ◽  
William Spindler
Keyword(s):  
Individual Cell ◽  
Cell Performance ◽  
Peak Shaving ◽  
Lead Acid

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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