DocumentCode :
2226175
Title :
Structure of neural networks for industrial character reader
Author :
Hata, Seiji ; Seino, K. ; Yagisawa, Akira
Author_Institution :
Fac. of Educ., Kagawa Univ., Takamatsu, Japan
fYear :
1993
fDate :
15-19 Nov 1993
Firstpage :
1888
Abstract :
Neural networks to recognize industrial characters are required to achieve high reading reliability. To achieve this high reliability, a method to control the structure of a neural network´s hidden layer has been introduced. The method defines the feature extraction functions of neurons in the hidden layer, and preliminary teaching is so constructed that it gives the hidden layer neurons defined properties. After the desired property attached to the hidden layer neurons, the ordinary backpropagation procedure refines the structure of the neural network
Keywords :
automatic optical inspection; backpropagation; character recognition; character recognition equipment; feature extraction; neural nets; backpropagation; feature extraction functions; high reading reliability; industrial character reader; neural network´s hidden layer; neural networks; preliminary teaching; Assembly systems; Character recognition; Computer network reliability; Computer science; Inspection; Manufacturing; Neural networks; Neurons; Production; Reliability engineering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics, Control, and Instrumentation, 1993. Proceedings of the IECON '93., International Conference on
Conference_Location :
Maui, HI
Print_ISBN :
0-7803-0891-3
Type :
conf
DOI :
10.1109/IECON.1993.339362
Filename :
339362
Link To Document :
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