DocumentCode :
3195362
Title :
Study on Soft-Sensing Model of Tower Crane Load Moment Based on Functional Link Neural Network
Author :
Guo, Quanmin ; Jia, Yongfeng
Author_Institution :
Sch. of Electron. Inf. Eng., Xi´´an Technol. Univ., Xi´´an, China
Volume :
3
fYear :
2010
fDate :
11-12 May 2010
Firstpage :
538
Lastpage :
541
Abstract :
In soft-sensing of tower crane load moment, the nonlinear relation between the load moment and the horizontal displacement of moment limiter is indicated by analysis of working principle of elastic steel plate type load moment limiter. This paper proposes a soft-sensing model based on functional link neural network (FLNN) with the horizontal displacement of moment limiter as input and the load moment as output. By adding some high-order terms, the model applies the single-layer network to realize the network supervised learning. The method has advantages of nonlinear approach ability and independent on accurate mathematical model, it can improve network learning speed and simplify the network structure, and provides a new way for On-line measurement of tower crane load moment. The implementation process of Monitor System of Load Moment based on FLNN about tower crane QTZ5012 is presented, the experimental research show that the maximum relative error of simulation curves is reduced to 2.02% and can satisfy the National standard GB5144-94.
Keywords :
cranes; mechanical engineering computing; neural nets; QTZ5012; functional link neural network; horizontal displacement; moment limiter; nonlinear approach; nonlinear relation; soft-sensing model; tower crane load moment; Cranes; Displacement measurement; Fasteners; Monitoring; Neural networks; Poles and towers; Power measurement; Protection; Steel; Switches; Soft-sensing Model; functional link neural network; load moment; tower crane;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-7279-6
Electronic_ISBN :
978-1-4244-7280-2
Type :
conf
DOI :
10.1109/ICICTA.2010.566
Filename :
5522866
Link To Document :
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