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The algorithm of CFNN image data fusion in multi-sensor data fusion
Zeng, Xiaohong
2014
发表期刊Sensors and Transducers
卷号166期号:3页码:197-202
摘要CFNN hybrid system in Multi-sensor data fusion introduced fuzzy logic reasoning and neural network adaptive, self-learning ability, and using fuzzy neurons, so networking skills appropriate to adjust the input and output fuzzy membership function, and can dynamically optimize fuzzy reasoning in global by means of compensated logic algorithm, to make the network more fault tolerance, stability and speed up training. This paper introduces a mathematical model of the image data fusion, and elaborates CFNN image data fusion algorithms, simulation results show that this method can significantly improve the quality of the image data fusion, data fusion with other existing algorithms have a very significant effect. © 2014 IFSA Publishing, S. L.
关键词Algorithms Data fusion Fault tolerance Fuzzy logic Hybrid systems Image fusion Membership functions Personnel training CFNN Fuzzy logic reasoning Fuzzy membership function Image data fusion Image filtering Multiple sensors Multisensor data fusion Self-learning ability
收录类别EI
语种英语
出版者International Frequency Sensor Association
EI入藏号20151100630394
EI分类号721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory ; 722.4 Digital Computers and Systems ; 723.2 Data Processing and Image Processing ; 901.2 Education ; 921 Mathematics
原始文献类型Journal article (JA)
文献类型期刊论文
条目标识符https://ir.cqcet.edu.cn/handle/39TD4454/3083
专题重庆电子科技职业大学
作者单位Chongqing College of Electronic Engineering, Chongqing College Town, Shapingba District, Chongqing City, China
第一作者单位重庆电子科技职业大学
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Zeng, Xiaohong. The algorithm of CFNN image data fusion in multi-sensor data fusion[J]. Sensors and Transducers,2014,166(3):197-202.
APA Zeng, Xiaohong.(2014).The algorithm of CFNN image data fusion in multi-sensor data fusion.Sensors and Transducers,166(3),197-202.
MLA Zeng, Xiaohong."The algorithm of CFNN image data fusion in multi-sensor data fusion".Sensors and Transducers 166.3(2014):197-202.
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