Research on information fusion optimization algorithm of ceramic shuttle kiln temperature based on multi-sensor

2017 ◽  
Vol 17 (4) ◽  
pp. 655-664
Author(s):  
Yong-Hong Zhu ◽  
Yi-Feng Zhao ◽  
Jun-Xiang Wang
Keyword(s):  
Information Fusion ◽  
Optimization Algorithm ◽  
Kiln Temperature

D-S Algorithm Based on SCO for Matching Fragments

2013 ◽  
Vol 411-414 ◽  
pp. 1876-1879 ◽  
Author(s):  
Jia Ze Sun ◽  
Guo Hua Geng ◽  
Xiao Ying Pan
Keyword(s):  
Information Fusion ◽  
Optimization Algorithm ◽  
Social Cognitive ◽  
Evidence Theory ◽  
Training Data ◽  
Uncertain Information ◽  
3D Objects ◽  
Optimized Model ◽  
Different Sources ◽  
Cognitive Optimization

The Dempster-Shafer (D-S) evidence theory is an effective method for uncertain information fusion. Because multiple evidences from different sources of different importance or reliability in the reassembling fractured 3D objects are not equally important when they are combined. This paper presents a social cognitive optimization algorithm (SCO) to generate optimal evidence weight values based on historical training data. In the algorithm, a constrained nonlinear optimized model is established, which is solved by SCO. Compared with the two methods, optimization weight D-S proves more effective than the traditional D-S.

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