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
推荐引用方式 GB/T 7714
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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