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

✍ Scribed by M Onder Efe; Okyay Kaynak

Publisher: Elsevier Science
Year: 2000
Tongue: English
Weight: 299 KB
Volume: 30
Category: Article
ISSN: 0921-8890
DOI: 10.1016/s0921-8890(99)00087-1

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✦ Synopsis

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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