ANN Training Method with a Small Number of Examples Used for Robots Control

  • Emilia Ciupan Technical University of Cluj-Napoca Romania, 400641 Cluj-Napoca, Bd. Muncii, 103-105
  • Florin Lungu Technical University of Cluj-Napoca Romania, 400641 Cluj-Napoca, Bd. Muncii, 103-105
  • Cornel Ciupan Technical University of Cluj-Napoca Romania, 400641 Cluj-Napoca, Bd. Muncii, 103-105

Abstract

This paper presents a method for obtaining a neural model used in industrial robots control. The method refers to the forming of a small number of examples used in the training of a neural network that lead to the creation of a suitable model. This paper constitutes a development of the work [2] in order to increase the opportunities for its application in various fields. The description of the method is generally done, without relying on a specific application in the domain of industrial robots. The testing and the validation of the shown method were completed using the example of a system in which the relationship between inputs and outputs is described by means of mathematical functions. The set of learning examples, generated through the proposed method, served to the ANN training by a cross-validation technique, in case of these functions. The evaluation of the proposed method has been done by analysing the results obtained by applying it compared to those obtained with a known method, namely the uniform generation of training examples. The use of the method in the field of industrial robots’ control was illustrated by a concrete application in the case of a robot with 6 degrees of freedom.

References

[1] Campean E.; Itul T.P.; Tanase I.; Pisla A.; Workspace Generation for a 2 - DOF Parallel Mechanism Using Neural Networks, Applied Mechanics and Materials, Vol 162 (2012), pp 121-130, doi: 10.4028/www.scientific.net/AMM.162.121.
http://dx.doi.org/10.4028/www.scientific.net/AMM.162.121

[2] Ciupan E.; Lungu F.; Ciupan C. (2014); A Method for Training a Neural Network with a Small Number of Examples Used for Robot Control, ICCCC 2014, 5th International Conference on Computers Communications & Control, Romania, Oradea, May 6-10.

[3] Dumitrache, I. (2008); From Model-Based Strategies to Intelligent Control Systems, Proc. of 9th WSEAS International Conference on Automation and Information, 408-415.

[4] Feng Y.; Wanh Y.; Yang Y. (2012); Inverse Kinematics Solution for Robot Manipulator based on Neural Network under Joint Subspace, International Journal of Computers Communications & Control, 7(3):459-472.
http://dx.doi.org/10.15837/ijccc.2012.3.1387

[5] Koker R. (2013); A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization, Information Sciences, 222, 528-543.
http://dx.doi.org/10.1016/j.ins.2012.07.051

[6] Lewis F. L.; Jagannathan S.; Yesildirak A. (1998); Neural network control for robots manipulators and nonlinear systems, Tylor & Francis.

[7] Negrean I.; Vuscan I.; Haiduc N. (1998); Kinematic and Dynamic Modelling, Editura Didactica si Pedagogica, Bucuresti, ISBN 973-30-5958-7.

[8] Zilouchian A.; Jamshidi M. (2001); Intelligent Control Systems using Soft Computing Methodologies, CRC Press LLC, ISBN 0-8493-1875-0.
Published
2015-07-01
How to Cite
CIUPAN, Emilia; LUNGU, Florin; CIUPAN, Cornel. ANN Training Method with a Small Number of Examples Used for Robots Control. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 10, n. 5, p. 643-653, july 2015. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2027>. Date accessed: 05 july 2020. doi: https://doi.org/10.15837/ijccc.2015.5.2027.

Keywords

ANN, training, method, small number, robot, control