|Table of Contents|

Robustness design of new templates for gray-scale logic converse implication operation CNN

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2014年04期
Page:
490-495
Research Field:
Publishing date:

Info

Title:
Robustness design of new templates for gray-scale logic converse implication operation CNN
Author(s):
Zhang Qun1Min Lequan12
1.School of Automation and Electrical Engineering; 2.School of Mathematics and Physics, University of Science and Technology Beijing,Beijing 100083,China
Keywords:
cellular neural network gray-scale images logic converse implication operation template robustness design template parameter
PACS:
TP183
DOI:
-
Abstract:
To solve the logical converse implication operation between two gray-scale images,a kind of gray-scale logic converse implication operation cellular neural network(GLCIO CNN)is studied here.According to the converse implication operation,the Local Rules of GLCIO CNN is proposed.A theorem is given to design the robustness of the templates of GLCIO CNN.The theorem is proved.The theorem provides parameter inequalities for selecting suitable template parameters of GLCIO CNN to implement the corresponding tasks.The numerical simulation examples verify the validity of GLCNIO CNN,and the methodology is efficient in practical applications for computer digital image processing.

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Last Update: 2014-08-31