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RETRACTED ARTICLE: Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

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This article was retracted on 19 November 2018

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Abstract

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR poly and SVR rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.

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Change history

  • 19 November 2018

    The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the articles cited [2, 3]) and authorship manipulation.

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Acknowledgments

This work is funded by the University of Malaya, Malaysia, under grant RP002D-13ICT.

Author information

Authors and Affiliations

  1. Faculty of Mechanical Engineering, Deparment for Mechatronics and Control, University of Niš, Aleksandra Medvedeva 14, 18000, Niš, Serbia

    Dalibor Petković & Nenad D. Pavlović

  2. Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), 46615-397, Chalous, Mazandaran, Iran

    Shahaboddin Shamshirband

  3. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahaboddin Shamshirband, Tan Fong Ang & Nor Badrul Anuar

  4. Department of Information System, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Hadi Saboohi

  5. Computer Engineering Department, Islamic Azad University of Karaj, Karaj, Iran

    Hadi Saboohi

Authors
  1. Dalibor Petković
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  2. Shahaboddin Shamshirband
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  3. Hadi Saboohi
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  4. Tan Fong Ang
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  5. Nor Badrul Anuar
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  6. Nenad D. Pavlović
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Corresponding author

Correspondence to Dalibor Petković.

Additional information

The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the articles cited [2-3]) and authorship manipulation. The authors have not responded to correspondence about this retraction.

References

1. Petkovic, D., Shamshirband, S., Saboohi, H. et al. Appl Intell (2014) 41: 887. https://doi.org/10.1007/s10489-014-0574-5

2. Petkovic, D., Shamshirband, S., Saboohi, H. et al. Infrared Physics & Technology (2014) 65: 94-102. https://doi.org/10.1016/j.infrared.2014.04.005

3. Ramedani, Z., Omid, M., Keyhani, A. et al. Renewable and Sustainable Energy Reviews (2014) 39: 1005-1011. https://doi.org/10.1016/j.rser.2014.07.108

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Petković, D., Shamshirband, S., Saboohi, H. et al. RETRACTED ARTICLE: Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper. Appl Intell 41, 887–896 (2014). https://doi.org/10.1007/s10489-014-0574-5

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  • DOI: https://doi.org/10.1007/s10489-014-0574-5

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