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RAS
2000

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

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A comparative study of soft-computing methodologies in identification of robotic manipulators
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.
Mehmet Önder Efe, Okyay Kaynak
Added 19 Dec 2010
Updated 19 Dec 2010
Type Journal
Year 2000
Where RAS
Authors Mehmet Önder Efe, Okyay Kaynak
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