Retraction Note to: Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper (Applied Intelligence, (2014), 41, 3, (887-896), 10.1007/s10489-014-0574-5);Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

Applied Intelligence

Volumn 41, Issue 3, 2014, Pages 887-896

  Petković, Dalibor ^a   Shamshirband, Shahaboddin ^b,c   Saboohi, Hadi ^c,d   Ang, Tan Fong ^c   Anuar, Nor Badrul ^c   Pavlović, Nenad D ^a  

^a UNIVERSITY OF NI   (Serbia)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

^c UNIVERSITY OF MALAYA   (Malaysia)

^d ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Adaptive compliant gripper; Embedded sensors; Object recognizing; Soft computing; Support vector regression

Indexed keywords

CONTROLLERS; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; SOFT COMPUTING;

CONVENTIONAL CONTROL; EMBEDDED SENSORS; GENERALIZATION ERROR BOUNDS; OBJECT RECOGNIZING; PREDICTIVE ACCURACY; RADIAL BASIS FUNCTION(RBF); SOFT COMPUTING METHODOLOGIES; SUPPORT VECTOR REGRESSION (SVR);

GRIPPERS;

EID: 84918840988     PISSN: 0924669X     EISSN: 15737497     Source Type: Journal    
DOI: 10.1007/s10489-018-1367-z     Document Type: Erratum

References (41)

1. Petković D, Pavlović ND (2012) A new principle of adaptive compliant gripper, mechanisms, Series: transmission and applications, mechanisms and machine science, vol 3. Springer, pp 143–150. XVI, ISBN 978-94-007-2726-7
2. Becedas J, Gonzales E, Payo I, Feliu V (2011) On line visual-grasping system based on a gripper with two flexible fingers, preprints of the 18th IFAC world congress Milano (Italy) August 28 - September 2, p 14675–14680
3. Russell RA, Wijaya JA (2003) Object location and recognition using whisker sensors, Australian conference on robotics and automation, CD-ROM. In: Proceedings ISBN
4. E. Baccarelli, N. Cordeschi, T. Patriarca (2011) QoS Stochastic Traffic Engineering for the wireless support of real-time streaming applications, 56(1), 2012, pp. 287–302. doi:10.1016/j.comnet.2011.09.010
5. Chitta S, Piccoli M, Sturm J (2010) Tactile object class and internal state recognition for mobile manipulation. IEEE Int Conf Robot Autom (ICRA):2342–2348. Anchorage, AK, 3–7 May 2010
6. D. Amendola, F. De Rango, Kh. Massri, A. Vitaletti (2014) Efficient Neighbor Discovery in RFID Based Devices Over Resource-Constrained DTN Networks, IEEE ICC 2014, pp. 1–6.
7. Allen P, Bajcsy R (1987) Robotic object recognition using vision and touch. In: Proceedings of the 9th international joint conference on artificial intelligence
8. Abdullah SC, Wada J, Ohka M, Yussof H (2012) Object exploration using a three-axis tactile sensing information. J Comput Sci 7(4):499–504
9. Jimenez AR, Soembagijo AS, Reynaerts D, Brussel HV, Ceres R, Pons JL (1997) Featureless classification of tactile contacts in a gripper using neural networks. Sensors Actuators 62:488–491
10. Bohg J, Kragic D (2010) Learning grasping points with shape context. Robot Auton Syst 58:362–377
11. Radig B, Florczyk S (2001) A new contour-based approach to object recognition for assembly line robots, pattern recognition – 23rd DAGM symposium. Springer, pp 329–336
12. Katic D, Vikobratovic M (1999) The application of connectionist structures to learning impedance control in robotic contact tasks. Appl Intell 7:315–326
13. Sanz PJ, Inqesta JM, Del pobil AP (1997) Planar grasping characterization based on curvature-symmetry fusion. Appl Intell 10:25–36
14. An S-Y, Kang J-G, Choi W-S, An S-Y O-Y, Kang J-G, Choi W-S, Oh S-Y (2011) A neural network based retrainable framework for robust object recognition with application to mobile robotics. Appl Intell 35:190–210
15. Issa M, Petković D, Pavlović ND, Zentner L (2011) Embedded-sensing elements made of conductive silicone rubber for compliant robotic joint, 56th Internationales Wissenschaftliches Kolloquium TU Ilmenau, (CD-ROM), Ilmenau, paper id 1231
16. Petković D, Issa M, Pavlović ND, Zentner L (2013) Intelligent rotational direction control of passive robotic joint with embedded sensors. Expert Syst Appl, ISSN 0957–4174 40 (4):1265– 1273
17. Shamshirbanda S, Patelc A, Anuarb NB, Kiahb MLM, Abraham A Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks. Eng. Appl. Artif. Intell. 32:228–241. June 2014
18. Wua S, Wanga Y, Cheng S (2013) Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system. Neurocomputing 102:163–175
19. Petković D, Ćojbašić ž, Nikolić V, Shamshirband S, Kiah MLM, Anuar NB, Abdul Wahab AW (2014) Adaptive neuro- fuzzy maximal power extraction of wind turbine with continuously variable transmission. Energy 64:868–874
20. Petković D, Pavlović NT, Shamshirband S, Mat Kiah ML, Anuar NB, Idna Idris MY Adaptive neuro-fuzzy estimation of optimal lens system parameters. Opt Lasers Eng 55:84–93
21. Lu K, Zhao J, Cai D (2006) An algorithm for semi-supervised learning in image retrieval. Pattern Recog 39:717–720
22. Leng Y, Xu X, Qi G (2013) Combining active learning and semi-supervised learning to construct SVM classifier. Knowl-Based Syst 44:121–131
23. Petković D, Ćojbašić ž, Nikolić V (2013) Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation. Renew Sust Energ Rev 28:191–195
24. Shamshirband S, Anuar NB, Mat Kiah ML, Patel A An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique. Eng Appl Artif Intell 26(9):2105–2127
25. Ornella L, Tapia E (2010) Supervised machine learning and heterotic classification of maize (Zea mays L.); using molecular marker data. Comput Electron Agric 74:250–257
26. Jain P, Garibaldib JM, Hirst JD (2009) Supervised machine learning algorithms for protein structure classification. Comput Biol Chem 33:216–223
27. Balahura A, Turchi M (2013) Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Comput Speech Lang 28(1):56–75
28. Chakraborty S (2011) Bayesian semi-supervised learning with support vector machine. Stat Methodol 8:68–82
29. Rajasekaran S, Gayathri S, Lee T-L (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35:1578–1587
30. Yang H, Huang K, King I, Lyu MR (2009) Localized support vector regression for time series prediction. Neurocomputing 72:2659–2669
31. Wei Z, Tao T, ZhuoShu D, Zio E (2013) A dynamic particle filter-support vector regressio n method for reliability prediction. Reliab Eng Syst Saf 119:109–116
32. Zhang L, Zhou W-D, Chang P-C, Yang J-W, Li F-Z (2013) Iterated time series prediction with multiple support vector regression models. Neurocomputing 99:411–422
33. Petković D, Pavlović ND (2011) Investigation and adaptive neuro-fuzzy estimation of mechanical/electrical properties of conductive silicone rubber, 34th international conference on production engineering, Nis, Serbia, p 385–388
34. Issa M, Petković D, Pavlović ND, Zentner L (2013) Sensor elements made of conductive silicone rubber for passively compliant gripper. Int J Adv Manuf Technol 69(5):1527–1536. doi:10.1007/s00170-013-5085-8
35. Petković D, Pavlović ND, Shamshirband S, Anuar Nor B (2013) Development of a new type of passively adaptive compliant gripper. Ind Robot 40(6):610–623
36. Petković D, Pavlović ND (2011) Investigation and Adaptive Neuro-Fuzzy Estimation of Mechanical/Electrical Properties of Conductive Silicone Rubber, 34th International Conference on Production Engineering, Nis, Serbia, pp 385–388
37. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
38. Suykens JAK, Vandewalle J (1999) Least Squares Support Vector Machine Classifiers. Neural Process Lett 9:293–300
39. Lin J-Y, Cheng C-T, Chau K-W (2006) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51:599–612
40. Choy KY, Chan CW (2003) Modelling of river discharges and rainfall using radial basis function networks based on support vector regression. Int J Syst Sci 34:763–773
41. Smola A, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222