Adaptive MIMO Neural Network Model Optimized by Differential Evolution Algorithm for Manipulator Kinematic System Identification

DOI

10.2991/acta-14.2014.6How to use a DOI?

Keywords

Differential Evolution (DE); Back-Propagation Algorithm; Nonlinear System Identification; Robot Manipulator.

Abstract

In this paper, an adaptive MIMO neural network model is used for simultaneously modeling and identifying the forward kinematics of a 3-DOF robot manipulator. The nonlinear features of the robot manipulator kinematics system are modeled by an adaptive MIMO neural network model based on differential evolution algorithm. A differential evolution algorithm is used to optimally generate the appropriate neural weights so as to perfectly characterize the nonlinear features of the forward kinematics of a 3-DOF robot manipulator. This paper supports the performance of the proposed differential evolution algorithm in comparison with the conventional back-propagation algorithm. The results show that the proposed adaptive MIMO neural network model trained by the differential evolution algorithm for identifying the forward kinematics of a 3-DOF robot manipulator is successfully modeled and performed well.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Son Nguyen Ngoc
AU  - Anh Ho Pham Huy
PY  - 2014/06
DA  - 2014/06
TI  - Adaptive MIMO Neural Network Model Optimized by Differential Evolution Algorithm for Manipulator Kinematic System Identification
BT  - 2014 International Conference on Automatic Control Theory and Application
PB  - Atlantis Press
SP  - 23
EP  - 26
SN  - 2352-5398
UR  - https://doi.org/10.2991/acta-14.2014.6
DO  - 10.2991/acta-14.2014.6
ID  - NguyenNgoc2014/06
ER  -