Title :
A multi-level backpropagation network for pattern recognition systems
Author :
Chen, C.Y. ; Hwang, C.J.
Author_Institution :
Dept. of Comput. Eng. & Sci., Yuan-Ze Inst. of Technol., Chungli, Taiwan
fDate :
27 Jun-2 Jul 1994
Abstract :
The backpropagation network (BPN) is now widely used in the field of pattern recognition because this artificial neural network can classify complex patterns and perform nontrivial mapping functions. In this paper, we propose a multi-level backpropagation network (MLBPN) model as a classifier for practical pattern recognition systems. The described model reserves the benefits of the BPN and derives the extra benefits of this MLBPN with two fold: (1) the MLBPN can reduce the complexity of BPN, and (2) a speed-up of the recognition process is attained. The experimental results verify these characteristics and show that the MLBPN model is a practical classifier for pattern recognition systems
Keywords :
backpropagation; feature extraction; feedforward neural nets; pattern recognition; feature extraction; multilevel backpropagation network; neural network; pattern recognition systems; Artificial neural networks; Backpropagation; Character recognition; Feature extraction; Frequency locked loops; Pattern recognition;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374724
