Fuzzy Data Fusion for Updating Information in Modeling Drivers’ Choice Behavior

Author(s):  
Mauro Dell’Orco ◽  
Mario Marinelli
1997 ◽  
Vol 30 (6) ◽  
pp. 943-948 ◽  
Author(s):  
F Alonge ◽  
G.Di Bemardo ◽  
F.M Raimondi ◽  
F Italia ◽  
M Lavorgna

2008 ◽  
Vol 375-376 ◽  
pp. 626-630
Author(s):  
Bang Yan Ye ◽  
Jian Ping Liu ◽  
Rui Tao Peng ◽  
Yong Tang ◽  
Xue Zhi Zhao

For detecting gradual tool wear state on line, the methods of Wavelet Fuzzy Neural Network, Regression Neural Network and Sample Classification Fuzzy Neural Network by detecting cutting force, motor power of machine tool and AE signal respectively are presented. Although these methods are not difficult to come true and processed accurately and rapidly, it is difficult to obtain comprehensive information of machining and exact value of tool wear when using single method of intelligent modeling and single signal detecting. For this purpose, fuzzy inference technique is adopted to fuse the recognized data. Emulation experiment is carried out by using Matlab software platform and this method is verified to be feasible. Experimental result indicates that by applying fuzzy data fusion, we can get an exact tool wear forecast rapidly.


2014 ◽  
Vol 602-605 ◽  
pp. 1127-1130
Author(s):  
Chao Wang ◽  
Tao Tan ◽  
Xuan Yin ◽  
Nan Chen ◽  
Tong Xin Xiao

To solve the unstable and insufficient energy supply problems in Smart Home Controlling System and improve the he cooperation between several sensors with different functions and parameters, a new energy supply module based on the vibration in the air and the fuzzy data fusion theory applied to the cooperation among sensors are discussed in this paper. The test result proves that this kind of module works well to provide enough energy preservation for the whole WSN system and data fusion theory enhances the credibility and preciseness of the surveillance result.


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