Pattern Recognition System Insensitive to Translation and Rotation Using Neural Networks with Signum Functions

Abstract

In this paper, a pattern recognition system by neurons with signum function is considered. The entire pattern recognition system is constructed by an invariance net of neurons with signum function and a trainable descrambler with CONE. The invariance net can be designed to produce a set of outputs that are insensitive to translation and rotation of the input patterns. The original patterns can be reproduced in standard position by the trainable descrambler. The system presented here produces a noise tolerant mapping and has a fast convergence rate with CNR algorithm.