scholarly journals K-Means-Based Polynomial-Radial Basis Function Neural Network Using Space Search Algorithm: Design and Comparative Studies

2011 ◽  
Vol 17 (8) ◽  
pp. 731-738
Author(s):  
Wook-Dong Kim ◽  
Sung-Kwun Oh
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Comparative Studies ◽  
Search Algorithm ◽  
Algorithm Design ◽  
Radial Basis

scholarly journals Anti-swing control of the overhead crane system based on the harmony search radial basis function neural network algorithm

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983445 ◽  
Author(s):  
Yubin Miao ◽  
Fenglin Xu ◽  
Yanwei Hu ◽  
Jianping An ◽  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Motion Path ◽  
Motion Trajectory ◽  
Good Convergence ◽  
Radial Basis

The swing of the grab is a main factor affecting the working efficiency of overhead cranes. Thus, planning the optimal motion path can reduce the adverse effects caused by the grab swing and improve the loading and unloading efficiency. The dynamic model of the trolley–grab system is established by considering factors like the change of rope length, wind load, and air resistance. First, the radial basis function neural network is applied to generate a feasible motion trajectory of the crane trolley. Taking the swing angle and angular velocity of the grab at the discharge point as evaluation, the harmony search algorithm is then applied to optimize the neural network parameters and obtain the optimal anti-swing motion trajectory. The numerical simulation and practical testing results show that the harmony search–radial basis function algorithm generates a smooth motion trajectory with good convergence, achieving anti-swing control of the trolley–grab system.

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