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

被引：24

作者：

Efe, MO ^{[1
]}

Kaynak, O ^{[1
]}

机构：

[1] Bogazici Univ, Dept Elect & Elect Engn, Mechatron Res & Appl Ctr, TR-80815 Bebek, Istanbul, Turkey

来源：

ROBOTICS AND AUTONOMOUS SYSTEMS | 2000年 / 30卷 / 03期

关键词：

neural networks; fuzzy systems; identification; robotics; comparison;

D O I：

10.1016/S0921-8890(99)00087-1

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2000 Elsevier Science B.V. All rights reserved.

引用

页码：221 / 230

页数：10

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