|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 Qun1; Min Lequan1; 2
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.

References:

[1] Chua L O,Yang L.Cellular neural networks:theory[J].IEEE Transactions on Circuits Systems,1988,35(10):1257-1290.
[2]Dudek P J.An asynchronous cellular logic network for trigger-wave image processing on fine-grain massively parallel arrays[J].IEEE Transactions on Circuits and Systems-Ⅱ:Analog and Digital Signal Processing,2006,53:354-358.
[3]Sebastian Sudheer K,Sabir M.Adaptive function projective synchronization of two-cell Quantum-CNN chaotic oscillators with uncertain parameters[J].Physics Letters A,2009,373(21):1847-1851.
[4]Arun P V,Katiyar S K.Cellular neural network-based hybrid approach toward automatic image registration[J].Journal of Applied Remote Sensing,2013,7:1-11.
[5]Goras L,Chua L O.Turing patterns in CNNs—part Ⅱ:equations and behaviours[J].IEEE Transactions on Circuits and Systems-Ⅰ,1995,42(10):612-626.
[6]Li Huaqing,Liao Xiaofeng,Li Chuandong,et al.Edge detection of noisy images based on cellular neural networks[J].Commun Nonlinear Sci Numer Simulat,2011,16:3746-3759.
[7]Min Lequn,Lei Ming,Dong Xisong.New templates of CNN extracting corners of objects in gray-scale images[J].Journal of University of Science and Technology Beijing,2003,10(3):73-75.
[8]Liu Jinzhu,Min Lequan.Design for CNN templates with performances of global connectivity detection[J].Communications in Theoretical Physics,2004,41(1):151-156.
[9]赵建业,王新,余道衡.基于Gibbs图像模型的细胞神经网络模板设计法[J].电子学报,2000,28(1):48-52.
Zhao Jianye,Wang Xin,Yu Daoheng.A template design method for cellular neural networks based on Gibbs image model[J].Chinese Journal of Electronics,2000,28(1):48-52.
[10]Li Chunguang,Xu Hongbing,Liao Xiaofeng,et al.Tabu search for CNN template learning[J].Neurocomputing,2003,51(4):475-479.
[11]Fasih A,Chedjou J C,Kyamakya K.Cellular neural network trainer and template optimization for advanced robot locomotion,based on genetic algorithm[A].Proceedings of the 15th International Conference on Mechatronics and Machine Vision in Practice[C].Auckland,New Zealand:IEEE Press,2008:317-322.
[12]Fornarelli G,Giaquinto A.Adaptive particle swarm optimization for CNN associative memories design[J].Neurocomputing,2009,72(16-18):3851-3862.
[13]Basturk A,Gunay E.Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm[J].Expert Systems with Applications,2009,36(2):2645-2650.
[14]Chua L O,Thiran P.An analytic method for designing simple cellular neural networks[J].IEEE Transactions on Circuits and Systems,1991,38(11):1332-1341.
[15]Deng Shaojiang,Tian Yuan,Hu Xipeng,et al.Application of new advanced CNN structure with adaptive thresholds to color edge detection[J].Commun Nonlinear Sci Numer Simulat,2012,17:1637-1648.
[16]Liu Jinzhu,Min Lequan.Robust design of bipolar wave cellular neural network with applications[J].International Journal of Modelling,Identification and Control,2010,11(1/2):99-106.
[17]沙莎,刘金珠,闵乐泉.复合4邻域圈提取CNN的鲁棒性设计[J].计算机工程与应用,2011,47(2):197-199.
Sha Sha,Liu Jinzhu,Min Lequan.Robust designs for compound 4 adjoining points circle detection[J].Computer Engineering and Application,2011,47(2):197-199.

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Last Update: 2014-08-31