Title :
A comparison of neural network models for pattern recognition
Author_Institution :
Dept. of Electr. & Comput. Eng., Southeastern Massachusetts Univ., North, MA, USA
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;
Conference_Titel :
Pattern Recognition, 1990. Proceedings., 10th International Conference on
Conference_Location :
Atlantic City, NJ
Print_ISBN :
0-8186-2062-5
DOI :
10.1109/ICPR.1990.119327
