Forward kinematics of the 6-6 general parallel manipulator using Real Coded Genetic Algorithms

Forward kinematics modelling of a parallel ankle rehabilitation robot using modified fuzzy inference

“…Similarly fuzzy inference system was preferred over GA and neural network (NN) approaches. This is because GA is time consuming [36] and not very accurate [42] at the same time NN cannot solve for the datasets which are of lower resolution than the training database and hence requires large database with low resolution for training. The fuzzy systems have two types of inference mechanisms namely, Takagi-Sugeno and Mamdani fuzzy systems.…”

“…GA based approach is time consuming [36] and lacks in the accuracy [42] whereas NN algorithms requires large database for training. In the light of above, we have proposed a fuzzy logic based inference mechanism which is less computationally expensive and is more accurate following some modifications as to be explained in Section 5.…”

Real-time solution of the forward kinematics for a parallel haptic device using a numerical approach based on neural networks

“…(12) indicates that the estimated p 0 will never exceed the overlap region O pq , and as such, net p and net q are both the correct PENNs to calculate the FKP. Thus, the boundary overlapping partition method will always guarantee a correct PENN for forward kinematics solution.…”

“…Wang proposed a differential evolution (DE) method to obtain a globally optimal solution for forward kinematics [11]. Moreover, genetic algorithm-based methods have been introduced by Rolland and Liu to solve the FKP of the Gough-Stewart platform [12,13].…”

“…Various numerical approaches have been studied in Refs. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] to obtain fast and efficient FKP solutions for parallel mechanisms. These approaches include iterative approaches, polynomial approximation approaches, optimization algorithmbased approaches, and neural network (NN)-based approaches.…”