A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

Table 3

System performance comparison between this study and other studies mentioned in the literature.

Study	System	DOF	Method	MSE	Hidden layers	Errors
Proposed	Denso	6	ANN		10	%; %; %
Luv et al., 2014 [10]	PUMA 560	6	ANN	1.217	30	%; %; %
Hasan et al., 2010 [2]	FANUC M-710i robot	6	ANN	~1	1	%; %; %
Toshani and Farrokhi, 2014 [4]	Simulation PA-10 robot	7	NNs with optimization	1	—	End effector 5 mm
Duka, 2014 [11]	Planar simulation	3	NNT	0.0054	1	—
Köker, 2013 [3]	Stanford	6	ANN	2.38	25	End effector 4.28 mm
Nanda et al., 2012 [5]	Simulation	3	ANN, FLANN	>1	20	—
Daya et al., 2010 [6]	simulation	2	NNT		2	—