A comparative study of soft-computing methodologies in identification of robotic manipulators

Abstract This paper investigates the identification of nonlinear systems by utilizing soft-computing approaches. As the identification methods, feedforward neural network architecture (FNN), radial basis function neural networks (RBFNN), Runge–Kutta neural networks (RKNN) and adaptive neuro-fuzzy inference systems (ANFIS) based identification mechanisms are studied and their performances are comparatively evaluated on a two degrees of freedom direct drive robotic manipulator.

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