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

Volume

5

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

3078

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICNN.1994.374724

Filename

374724