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.

• Type: Journal
• Year: 2000
• Where: RAS
• Authors: Mehmet Önder Efe, Okyay Kaynak
• Keywords: Feedforward Neural Network, Function Neural Networks, Neural Networks, RAS 2000