Two Theorems on the Robust Designs for Pattern Matching CNNs

Authors:
Bing Zhao;Weidong Li;Shu Jian;Lequan Min
Affiliations:
Applied Science School, University of Science and Technology Beijing, Beijing 100083, PR China;Applied Science School, University of Science and Technology Beijing, Beijing 100083, PR China;Applied Science School, University of Science and Technology Beijing, Beijing 100083, PR China;Applied Science School, University of Science and Technology Beijing, Beijing 100083, PR China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

The cellular neural/nonlinear network (CNN) has become a useful tool for image and signal processing, robotic and biological visions, and higher brain functions. Based on our previous research, this paper set up two new theorems of robust designs for Pattern Matching CNN in processing binary images, which provide parameter inequalities to determine parameter intervals for implementing the prescribed image processing function. Three numerical simulation examples are given.