An on-line algorithm for creating self-organizing fuzzy neural networks

2004 ◽  
Vol 17 (10) ◽  
pp. 1477-1493 ◽  
Author(s):  
Gang Leng ◽  
Girijesh Prasad ◽  
Thomas Martin McGinnity
2019 ◽  
Vol 49 (1) ◽  
pp. 14-26 ◽  
Author(s):  
Honggui Han ◽  
Lu Zhang ◽  
Xiaolong Wu ◽  
Junfei Qiao

2011 ◽  
Vol 19 (6) ◽  
pp. 1406-1413 ◽  
Author(s):  
李迪 LI Di ◽  
陈向坚 CHEN Xiang-jian ◽  
续志军 XU Zhi-jun ◽  
杨帆 YANG Fan ◽  
牛文达 NIU Wen-da

2014 ◽  
Vol 1046 ◽  
pp. 43-49
Author(s):  
Yi Yuan Shao ◽  
Fei Shao

A batch of operating parameters which need to be resolved on line are represented by operating modes.Operating mode optimization for copper flash smelting process based on fuzzy neural networks is presented. First of all, the optimal samples set is screened from the historical samples set. Then mode decomposition based on fuzzy neural networks is used, and chaos genetic algorithm is used to rake the optimal operating sub-pattern.This way is used to copper flash smelting process.The simulation result shows that this way can guide production.


1992 ◽  
Vol 56 (2) ◽  
pp. 415-439 ◽  
Author(s):  
T. YAMAGUCHI ◽  
T. TAKAGI ◽  
T. MITA

2010 ◽  
Vol 455 ◽  
pp. 539-543
Author(s):  
Ming Zhang ◽  
X.Q. Yang ◽  
Bo Zhao

In order to solve the difficulty of on-line measuring the surface roughness of workpiece under ultrasonic polishing, the artificial neural networks and fuzzy logic systems are introduced into the on-line prediction model of surface roughness. The surface roughness identification method based on fuzzy-neural networks is put forward and used to the process of plane polishing. In the end, the on-line prediction model of surface roughness is established. The actual ultrasonic polishing experiments show that the accuracy of this prediction model is up to 96.58%, which further evidence the feasibility of the on-line prediction model.


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