DocumentCode
3221258
Title
Inventory control neural network system
Author
Ezziane, Z.H. ; Mazouz, A.K. ; Han, C.
Author_Institution
Florida Atlantic Univ., Baca Raton, FL, USA
fYear
1993
fDate
7-9 Mar 1993
Firstpage
243
Lastpage
246
Abstract
A two-layer perceptron feedforward backpropagation network architecture with a minimum number of hidden neurons is designed using the backpropagation training algorithm for a noncomplex application, namely, controlling a plant inventory system. The design objective is to determine the number of hidden neurons and what type of data must be entered to get the backpropagation algorithm started. The convergence of the algorithm within a reasonable amount of time is sought. Test results are promising
Keywords
backpropagation; convergence; feedforward neural nets; industrial control; industrial plants; multilayer perceptrons; stock control; backpropagation training algorithm; convergence; feedforward; hidden neurons; neural network system; plant inventory system; two-layer perceptron; Backpropagation algorithms; Convergence; Flow production systems; Inventory control; Neural networks; Neurons; Pattern recognition; Production facilities; Raw materials; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
System Theory, 1993. Proceedings SSST '93., Twenty-Fifth Southeastern Symposium on
Conference_Location
Tuscaloosa, AL
ISSN
0094-2898
Print_ISBN
0-8186-3560-6
Type
conf
DOI
10.1109/SSST.1993.522779
Filename
522779
Link To Document