DocumentCode
3375413
Title
A comparison of neural network models for pattern recognition
Author
Chen, C.H.
Author_Institution
Dept. of Electr. & Comput. Eng., Southeastern Massachusetts Univ., North, MA, USA
Volume
ii
fYear
1990
fDate
16-21 Jun 1990
Firstpage
45
Abstract
A brief survey of the existing neural network models for signal/image processing and pattern recognition is presented. A comparison of the back-propagation algorithm for multilayer perception and an adaptive sample set construction procedure offered by Nestor´s restricted Coulomb energy network is presented. A performance comparison with real data for ultrasonic nondestructive evaluation of materials is presented
Keywords
neural nets; pattern recognition; picture processing; ultrasonic materials testing; Coulomb energy network; Nestor´s restricted; adaptive sample set; back-propagation algorithm; construction procedure; image processing; multilayer perception; neural network models; pattern recognition; performance comparison; signal processing; Computer networks; Hopfield neural networks; Multi-layer neural network; Multilayer perceptrons; Neural networks; Pattern recognition; Probability; Signal processing algorithms; Taxonomy; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1990. Proceedings., 10th International Conference on
Conference_Location
Atlantic City, NJ
Print_ISBN
0-8186-2062-5
Type
conf
DOI
10.1109/ICPR.1990.119327
Filename
119327
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