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Complexity
Volume 2017, Article ID 1895897, 14 pages
https://doi.org/10.1155/2017/1895897
Review Article

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

1Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
2Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China
3School of Engineering and Material Sciences, Queen Mary University of London, London, UK
4School of Engineering and Informatics, University of Sussex, Brighton, UK
5National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

Correspondence should be addressed to Chenguang Yang; gro.eeei@gnayc

Received 15 June 2017; Accepted 1 October 2017; Published 31 October 2017

Academic Editor: Thierry Floquet

Copyright © 2017 Yiming Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Gruau, D. Whitley, and L. Pyeatt, “A comparison between cellular encoding and direct encoding for genetic neural networks,” in Proceedings of the 1st annual conference on genetic programming, pp. 81–89, 1996.
  2. H. de Garis, “An artificial brain ATR's CAM-Brain Project aims to build/evolve an artificial brain with a million neural net modules inside a trillion cell Cellular Automata Machine,” New Generation Computing, vol. 12, no. 2, pp. 215–221, 1994. View at Publisher · View at Google Scholar · View at Scopus
  3. W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biology, vol. 5, no. 4, pp. 115–133, 1943. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Yang, S. S. Ge, C. Xiang, T. Chai, and T. H. Lee, “Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach,” IEEE Transactions on Neural Networks and Learning Systems, vol. 19, no. 11, pp. 1873–1886, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Kumar, R. K. Aggarwal, and J. D. Sharma, “Energy analysis of a building using artificial neural network: A review,” Energy and Buildings, vol. 65, pp. 352–358, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Alippi, C. De Russis, and V. Piuri, “A neural-network based control solution to air-fuel ratio control for automotive fuel-injection systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 33, no. 2, pp. 259–268, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. E. M. Azoff, Neural network time series forecasting of financial markets, John Wiley & Sons, Inc, 1994.
  8. I. Fister, P. N. Suganthan, J. Fister et al., “Artificial neural network regression as a local search heuristic for ensemble strategies in differential evolution,” Nonlinear Dynamics, vol. 84, no. 2, pp. 895–914, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. Y. Zhang, S. Li, and H. Guo, “A type of biased consensus-based distributed neural network for path planning,” Nonlinear Dynamics, vol. 89, no. 3, pp. 1803–1815, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  10. X. Shi, Z. Wang, and L. Han, “Finite-time stochastic synchronization of time-delay neural networks with noise disturbance,” Nonlinear Dynamics, vol. 88, no. 4, pp. 2747–2755, 2017. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. P. He and Y. Li, “H∞ synchronization of coupled reaction-diffusion neural networks with mixed delays,” Complexity, vol. 21, no. S2, pp. 42–53, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  12. Z. Tu, J. Cao, A. Alsaedi, F. E. Alsaadi, and T. Hayat, “Global Lagrange stability of complex-valued neural networks of neutral type with time-varying delays,” Complexity, vol. 21, no. S2, pp. 438–450, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. C. Wang, S. Guo, and Y. Xu, “Formation of autapse connected to neuron and its biological function,” Complexity, vol. 2017, Article ID 5436737, 9 pages, 2017. View at Publisher · View at Google Scholar
  14. J. D. J. Rubio, I. Elias, D. R. Cruz, and J. Pacheco, “Uniform stable radial basis function neural network for the prediction in two mechatronic processes,” Neurocomputing, vol. 227, pp. 122–130, 2017. View at Publisher · View at Google Scholar · View at Scopus
  15. J. d. Rubio, “USNFIS: Uniform stable neuro fuzzy inference system,” Neurocomputing, vol. 262, pp. 57–66, 2017. View at Publisher · View at Google Scholar
  16. Q. Liu, J. Yin, V. C. M. Leung, J.-H. Zhai, Z. Cai, and J. Lin, “Applying a new localized generalization error model to design neural networks trained with extreme learning machine,” Neural Computing and Applications, pp. 1–8, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. R. J. de Jesús, “Interpolation neural network model of a manufactured wind turbine,” Neural Computing and Applications, pp. 1–12, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Mu and D. Wang, “Neural-network-based adaptive guaranteed cost control of nonlinear dynamical systems with matched uncertainties,” Neurocomputing, vol. 245, pp. 46–54, 2017. View at Publisher · View at Google Scholar · View at Scopus
  19. Z. Lin, D. Ma, J. Meng, and L. Chen, “Relative ordering learning in spiking neural network for pattern recognition,” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  20. J. Yu, J. Sang, and X. Gao, “Machine learning and signal processing for big multimedia analysis,” Neurocomputing, vol. 257, pp. 1–4, 2017. View at Publisher · View at Google Scholar · View at Scopus
  21. T. Zhang, S. S. Ge, and C. C. Hang, “Adaptive neural network control for strict-feedback nonlinear systems using backstepping design,” Automatica, vol. 36, no. 12, pp. 1835–1846, 2000. View at Publisher · View at Google Scholar · View at Scopus
  22. S. S. Ge and T. Zhang, “Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback,” IEEE Transactions on Neural Networks and Learning Systems, vol. 14, no. 4, pp. 900–918, 2003. View at Publisher · View at Google Scholar · View at Scopus
  23. S. S. Ge, C. Yang, and T. H. Lee, “Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time,” IEEE Transactions on Neural Networks and Learning Systems, vol. 19, no. 9, pp. 1599–1614, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. F. W. Lewis, S. Jagannathan, and A. Yesildirak, Neural network control of robot manipulators and non-linear systems, CRC Press, 1998.
  25. S. Jagannathan and F. L. Lewis, “Identification of nonlinear dynamical systems using multilayered neural networks,” Automatica, vol. 32, no. 12, pp. 1707–1712, 1996. View at Publisher · View at Google Scholar · View at MathSciNet
  26. D. Vrabie and F. Lewis, “Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems,” Neural Networks, vol. 22, no. 3, pp. 237–246, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. C. Yang, S. S. Ge, and T. H. Lee, “Output feedback adaptive control of a class of nonlinear discrete-time systems with unknown control directions,” Automatica, vol. 45, no. 1, pp. 270–276, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. C. Yang, Z. Li, and J. Li, “Trajectory planning and optimized adaptive control for a class of wheeled inverted pendulum vehicle models,” IEEE Transactions on Cybernetics, vol. 43, no. 1, pp. 24–36, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. Jiang, C. Yang, and H. Ma, “A review of fuzzy logic and neural network based intelligent control design for discrete-time systems,” Discrete Dynamics in Nature and Society, Article ID 7217364, Art. ID 7217364, 11 pages, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  30. Y. Jiang, C. Yang, S.-L. Dai, and B. Ren, “Deterministic learning enhanced neutral network control of unmanned helicopter,” International Journal of Advanced Robotic Systems, vol. 13, no. 6, pp. 1–12, 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. Y. Jiang, Z. Liu, C. Chen, and Y. Zhang, “Adaptive robust fuzzy control for dual arm robot with unknown input deadzone nonlinearity,” Nonlinear Dynamics, vol. 81, no. 3, pp. 1301–1314, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  32. https://stemcellthailand.org/neurons-definition-function-neurotransmitters/.
  33. M. Defoort, T. Floquet, A. Kökösy, and W. Perruquetti, “Sliding-mode formation control for cooperative autonomous mobile robots,” IEEE Transactions on Industrial Electronics, vol. 55, no. 11, pp. 3944–3953, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. X. Liu, C. Yang, Z. Chen, M. Wang, and C. Su, “Neuro-adaptive observer based control of flexible joint robot,” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  35. F. Hamerlain, T. Floquet, and W. Perruquetti, “Experimental tests of a sliding mode controller for trajectory tracking of a car-like mobile robot,” Robotica, vol. 32, no. 1, pp. 63–76, 2014. View at Publisher · View at Google Scholar · View at Scopus
  36. R. J. de Jesús, “Discrete time control based in neural networks for pendulums,” Applied Soft Computing, 2017. View at Publisher · View at Google Scholar
  37. Y. Pan, M. J. Er, T. Sun, B. Xu, and H. Yu, “Adaptive fuzzy PD control with stable H∞ tracking guarantee,” Neurocomputing, vol. 237, pp. 71–78, 2017. View at Publisher · View at Google Scholar · View at Scopus
  38. R. J. de Jesús, “Adaptive least square control in discrete time of robotic arms,” Soft Computing, vol. 19, no. 12, pp. 3665–3676, 2015. View at Publisher · View at Google Scholar · View at Scopus
  39. S. Commuri, S. Jagannathan, and F. L. Lewis, “CMAC neural network control of robot manipulators,” Journal of Robotic Systems, vol. 14, no. 6, pp. 465–482, 1997. View at Publisher · View at Google Scholar · View at Scopus
  40. J. S. Albus, “Theoretical and experimental aspects of a cerebellar model,” Developmental Disabilities Research Reviews, vol. 17, pp. 93–101, 1972. View at Google Scholar
  41. B. Yang, R. Bao, and H. Han, “Robust hybrid control based on PD and novel CMAC with improved architecture and learning scheme for electric load simulator,” IEEE Transactions on Industrial Electronics, vol. 61, no. 10, pp. 5271–5279, 2014. View at Publisher · View at Google Scholar · View at Scopus
  42. S. Jagannathan and F. L. Lewis, “Multilayer discrete-time neural-net controller with guaranteed performance,” IEEE Transactions on Neural Networks and Learning Systems, vol. 7, no. 1, pp. 107–130, 1996. View at Publisher · View at Google Scholar
  43. S. S. Ge and J. Wang, “Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 13, no. 6, pp. 1409–1419, 2002. View at Publisher · View at Google Scholar · View at Scopus
  44. Y. H. Kim, F. L. Lewis, and C. T. Abdallah, “A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems,” Automatica, vol. 33, no. 8, pp. 1539–1543, 1997. View at Publisher · View at Google Scholar · View at MathSciNet
  45. J.-Q. Huang and F. L. Lewis, “Neural-network predictive control for nonlinear dynamic systems with time-delay,” IEEE Transactions on Neural Networks and Learning Systems, vol. 14, no. 2, pp. 377–389, 2003. View at Publisher · View at Google Scholar · View at Scopus
  46. S. S. Ge, F. Hong, and T. H. Lee, “Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 34, no. 1, pp. 499–516, 2004. View at Publisher · View at Google Scholar · View at Scopus
  47. J. Na, X. M. Ren, and H. Huang, “Time-delay positive feedback control for nonlinear time-delay systems with neural network compensation,” Zidonghua Xuebao/Acta Automatica Sinica, vol. 34, no. 9, pp. 1196–1202, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  48. J. Na, X. Ren, C. Shang, and Y. Guo, “Adaptive neural network predictive control for nonlinear pure feedback systems with input delay,” Journal of Process Control, vol. 22, no. 1, pp. 194–206, 2012. View at Publisher · View at Google Scholar · View at Scopus
  49. R. R. Selmic and F. L. Lewis, “Neural-network approximation of piecewise continuous functions: application to friction compensation,” IEEE Transactions on Neural Networks and Learning Systems, vol. 13, no. 3, pp. 745–751, 2002. View at Publisher · View at Google Scholar · View at Scopus
  50. H. Zhang and F. L. Lewis, “Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics,” Automatica, vol. 48, no. 7, pp. 1432–1439, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  51. J. Na, X. Ren, and D. Zheng, “Adaptive control for nonlinear pure-feedback systems with high-order sliding mode observer,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 3, pp. 370–382, 2013. View at Publisher · View at Google Scholar · View at Scopus
  52. J. Na, Q. Chen, X. Ren, and Y. Guo, “Adaptive prescribed performance motion control of servo mechanisms with friction compensation,” IEEE Transactions on Industrial Electronics, vol. 61, no. 1, pp. 486–494, 2014. View at Publisher · View at Google Scholar · View at Scopus
  53. J. Na, X. Ren, G. Herrmann, and Z. Qiao, “Adaptive neural dynamic surface control for servo systems with unknown dead-zone,” Control Engineering Practice, vol. 19, no. 11, pp. 1328–1343, 2011. View at Publisher · View at Google Scholar · View at Scopus
  54. G. Li, J. Na, D. P. Stoten, and X. Ren, “Adaptive neural network feedforward control for dynamically substructured systems,” IEEE Transactions on Control Systems Technology, vol. 22, no. 3, pp. 944–954, 2014. View at Publisher · View at Google Scholar · View at Scopus
  55. B. Xu, Z. Shi, C. Yang, and F. Sun, “Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form,” IEEE Transactions on Cybernetics, 2014. View at Publisher · View at Google Scholar · View at Scopus
  56. M. Chen and S. Ge, “Adaptive neural output feedback control of uncertain nonlinear systems with unknown hysteresis using disturbance observer,” IEEE Transactions on Industrial Electronics, 2015. View at Publisher · View at Google Scholar
  57. M. Chen and S. S. Ge, “Direct adaptive neural control for a class of uncertain nonaffine nonlinear systems based on disturbance observer,” IEEE Transactions on Cybernetics, vol. 43, no. 4, pp. 1213–1225, 2013. View at Publisher · View at Google Scholar · View at Scopus
  58. M. Chen, G. Tao, and B. Jiang, “Dynamic surface control using neural networks for a class of uncertain nonlinear systems with input saturation,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 9, pp. 2086–2097, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  59. P. J. Werbos, “Approximate dynamic programming for real-time control and neural modeling,” in Handbook of Intelligent Control, 1992. View at Google Scholar
  60. F.-Y. Wang, H. Zhang, and D. Liu, “Adaptive dynamic programming: an introduction,” IEEE Computational Intelligence Magazine, vol. 4, no. 2, pp. 39–47, 2009. View at Publisher · View at Google Scholar · View at Scopus
  61. F. L. Lewis, D. Vrabie, and K. . Vamvoudakis, “Reinforcement learning and feedback control: using natural decision methods to design optimal adaptive controllers,” IEEE Control Systems Magazine, vol. 32, no. 6, pp. 76–105, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  62. F. L. Lewis and D. Vrabie, “Reinforcement learning and adaptive dynamic programming for feedback control,” IEEE Circuits and Systems Magazine, vol. 9, no. 3, pp. 32–50, 2009. View at Publisher · View at Google Scholar · View at Scopus
  63. A. Al-Tamimi, F. L. Lewis, and M. Abu-Khalaf, “Discrete-time nonlinear HJB solution using approximate dynamic programming: convergence proof,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 38, no. 4, pp. 943–949, 2008. View at Publisher · View at Google Scholar · View at Scopus
  64. H. Zhang, Y. Luo, and D. Liu, “Neural-network-based near-optimal control for a class of discrete-time affine nonlinear systems with control constraints,” IEEE Transactions on Neural Networks and Learning Systems, vol. 20, no. 9, pp. 1490–1503, 2009. View at Publisher · View at Google Scholar · View at Scopus
  65. D. Wang, D. Liu, Q. Wei, D. Zhao, and N. Jin, “Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming,” Automatica, vol. 48, no. 8, pp. 1825–1832, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  66. H. He, Z. Ni, and J. Fu, “A three-network architecture for on-line learning and optimization based on adaptive dynamic programming,” Neurocomputing, vol. 78, no. 1, pp. 3–13, 2012. View at Publisher · View at Google Scholar · View at Scopus
  67. D. Liu and Q. Wei, “Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 3, pp. 621–634, 2014. View at Publisher · View at Google Scholar · View at Scopus
  68. D. Liu, X. Yang, D. Wang, and Q. Wei, “Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints,” IEEE Transactions on Cybernetics, vol. 45, no. 7, pp. 1372–1385, 2015. View at Publisher · View at Google Scholar · View at Scopus
  69. J. Fu, H. He, and X. Zhou, “Adaptive learning and control for MIMO system based on adaptive dynamic programming,” IEEE Transactions on Neural Networks and Learning Systems, vol. 22, no. 7, pp. 1133–1148, 2011. View at Publisher · View at Google Scholar · View at Scopus
  70. Y. Lv, J. Na, Q. Yang, X. Wu, and Y. Guo, “Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics,” International Journal of Control, vol. 89, no. 1, pp. 99–112, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  71. J. Na and G. Herrmann, “Online adaptive approximate optimal tracking control with simplified dual approximation structure for continuous-time unknown nonlinear systems,” IEEE/CAA Journal of Automatica Sinica, vol. 1, no. 4, pp. 412–422, 2014. View at Publisher · View at Google Scholar · View at Scopus
  72. X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447, 1999. View at Publisher · View at Google Scholar · View at Scopus
  73. S. F. Ding, H. Li, C. Y. Su, J. Z. Yu, and F. X. Jin, “Evolutionary artificial neural networks: a review,” Artificial Intelligence Review, vol. 39, no. 3, pp. 251–260, 2013. View at Publisher · View at Google Scholar · View at Scopus
  74. X. Yao and Y. Liu, “A new evolutionary system for evolving artificial neural networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 8, no. 3, pp. 694–713, 1997. View at Publisher · View at Google Scholar · View at Scopus
  75. Y. Li and A. Häußler, “Artificial evolution of neural networks and its application to feedback control,” Artificial Intelligence in Engineering, vol. 10, no. 2, pp. 143–152, 1996. View at Publisher · View at Google Scholar · View at Scopus
  76. C.-K. Goh, E.-J. Teoh, and K. C. Tan, “Hybrid multiobjective evolutionary design for artificial neural networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 19, no. 9, pp. 1531–1548, 2008. View at Publisher · View at Google Scholar · View at Scopus
  77. K. C. Tan and Y. Li, “Grey-box model identification via evolutionary computing,” Control Engineering Practice, vol. 10, no. 7, pp. 673–684, 2002. View at Publisher · View at Google Scholar · View at Scopus
  78. L. Wang, C. K. Tan, and C. M. Chew, Evolutionary robotics: from algorithms to implementations, Chew C M. Evolutionary robotics, from algorithms to implementations[M]. NJ, 2006.
  79. J. Zhang, Z.-H. Zhang, Y. Lin et al., “Evolutionary computation meets machine learning: a survey,” IEEE Computational Intelligence Magazine, vol. 6, no. 4, pp. 68–75, 2011. View at Publisher · View at Google Scholar · View at Scopus
  80. C. Yang, X. Wang, L. Cheng, and H. Ma, “Neural-learning-based telerobot control with guaranteed performance,” IEEE Transactions on Cybernetics, 2017. View at Publisher · View at Google Scholar · View at Scopus
  81. C. Yang, K. Huang, H. Cheng, Y. Li, and C. Su, “Haptic identification by ELM-controlled uncertain manipulator,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 8, 2017. View at Publisher · View at Google Scholar
  82. C. Yang, Z. Li, R. Cui, and B. Xu, “Neural network-based motion control of an underactuated wheeled inverted pendulum model,” IEEE Transactions on Neural Networks and Learning Systems, 2014. View at Publisher · View at Google Scholar · View at Scopus
  83. C. Yang, T. Teng, B. Xu, Z. Li, J. Na, and C. Su, “Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence,” International Journal of Control, Automation, and Systems, vol. 15, no. 4, pp. 1916–1924, 2017. View at Publisher · View at Google Scholar
  84. C. Yang, Y. Jiang, Z. Li, W. He, and C.-Y. Su, “Neural control of bimanual robots with guaranteed global stability and motion precision,” IEEE Transactions on Industrial Informatics, 2017. View at Publisher · View at Google Scholar
  85. R. Cui and W. Yan, “Mutual synchronization of multiple robot manipulators with unknown dynamics,” Journal of Intelligent & Robotic Systems, vol. 68, no. 2, pp. 105–119, 2012. View at Publisher · View at Google Scholar · View at Scopus
  86. L. Cheng, Z.-G. Hou, M. Tan, and W. J. Zhang, “Tracking control of a closed-chain five-bar robot with two degrees of freedom by integration of an approximation-based approach and mechanical design,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 5, pp. 1470–1479, 2012. View at Publisher · View at Google Scholar · View at Scopus
  87. C. Yang, X. Wang, Z. Li, Y. Li, and C. Su, “Teleoperation control based on combination of wave variable and neural networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017. View at Publisher · View at Google Scholar
  88. C. Yang, J. Luo, Y. Pan, Z. Liu, and C. Su, “Personalized variable gain control with tremor attenuation for robot teleoperation,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1–12, 2017. View at Publisher · View at Google Scholar
  89. L. Cheng, Z.-G. Hou, and M. Tan, “Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model,” Automatica, vol. 45, no. 10, pp. 2312–2318, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  90. W. He, Y. Dong, and C. Sun, “Adaptive Neural Impedance Control of a Robotic Manipulator with Input Saturation,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 3, pp. 334–344, 2016. View at Publisher · View at Google Scholar · View at Scopus
  91. W. He, A. O. David, Z. Yin, and C. Sun, “Neural network control of a robotic manipulator with input deadzone and output constraint,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015. View at Publisher · View at Google Scholar
  92. W. He, Z. Yin, and C. Sun, “Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function,” IEEE Transactions on Cybernetics, 2016. View at Publisher · View at Google Scholar · View at Scopus
  93. W. He, Y. Chen, and Z. Yin, “Adaptive neural network control of an uncertain robot with full-state constraints,” IEEE Transactions on Cybernetics, vol. 46, no. 3, pp. 620–629, 2016. View at Publisher · View at Google Scholar
  94. C. Sun, W. He, and J. Hong, “Neural Network Control of a Flexible Robotic Manipulator Using the Lumped Spring-Mass Model,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 8, pp. 1863–1874, 2017. View at Publisher · View at Google Scholar
  95. W. He, Y. Ouyang, and J. Hong, “Vibration Control of a Flexible Robotic Manipulator in the Presence of Input Deadzone,” IEEE Transactions on Industrial Informatics, vol. 13, no. 1, pp. 48–59, 2017. View at Publisher · View at Google Scholar · View at Scopus
  96. R. Cui, X. Zhang, and D. Cui, “Adaptive sliding-mode attitude control for autonomous underwater vehicles with input nonlinearities,” Ocean Engineering, vol. 123, pp. 45–54, 2016. View at Publisher · View at Google Scholar · View at Scopus
  97. R. Cui, C. Yang, Y. Li, and S. Sharma, “Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 6, pp. 1019–1029, 2017. View at Publisher · View at Google Scholar
  98. B. Xu, D. Wang, Y. Zhang, and Z. Shi, “DOB based neural control of flexible hypersonic flight vehicle considering wind effects,” IEEE Transactions on Industrial Electronics, vol. PP, no. 99, p. 1, 2017. View at Publisher · View at Google Scholar
  99. B. Xu, C. Yang, and Y. Pan, “Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 10, pp. 2563–2575, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  100. Y. Li, S. S. Ge, and C. Yang, “Learning impedance control for physical robot-environment interaction,” International Journal of Control, vol. 85, no. 2, pp. 182–193, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  101. Y. Li, S. S. Ge, Q. Zhang, and T. . Lee, “Neural networks impedance control of robots interacting with environments,” IET Control Theory & Applications, vol. 7, no. 11, pp. 1509–1519, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  102. Y. Li and S. S. Ge, “Impedance learning for robots interacting with unknown environments,” IEEE Transactions on Control Systems Technology, vol. 22, no. 4, pp. 1422–1432, 2014. View at Publisher · View at Google Scholar · View at Scopus
  103. C. Wang, Y. Li, S. S. Ge, and T. . Lee, “Optimal critic learning for robot control in time-varying environments,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 10, pp. 2301–2310, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  104. Y. Li, L. Chen, K. P. Tee, and Q. Li, “Reinforcement learning control for coordinated manipulation of multi-robots,” Neurocomputing, vol. 170, pp. 168–175, 2015. View at Publisher · View at Google Scholar · View at Scopus
  105. Y. Li and S. S. Ge, “Human—robot collaboration based on motion intention estimation,” IEEE/ASME Transactions on Mechatronics, vol. 19, no. 3, pp. 1007–1014, 2013. View at Publisher · View at Google Scholar · View at Scopus
  106. Y. Li, K. P. Tee, W. L. Chan, R. Yan, Y. Chua, and D. K. Limbu, “Continuous Role Adaptation for Human-Robot Shared Control,” IEEE Transactions on Robotics, vol. 31, no. 3, pp. 672–681, 2015. View at Publisher · View at Google Scholar · View at Scopus
  107. Y. Li, K. P. Tee, R. Yan, W. L. Chan, and Y. Wu, “A Framework of Human-Robot Coordination Based on Game Theory and Policy Iteration,” IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1408–1418, 2016. View at Publisher · View at Google Scholar · View at Scopus
  108. A. Clark, Surfing uncertainty: Prediction, action, and the embodied mind, Oxford University Press, 2015.
  109. J. Zhong, C. Weber, and S. Wermter, “Learning features and predictive transformation encoding based on a horizontal product model,” Neural Networks and Machine LearningICANN, pp. 539–546, 2012. View at Google Scholar
  110. J. Zhong, C. Weber, and S. Wermter, “A predictive network architecture for a robust and smooth robot docking behavior,” Journal of Behavioral Robotics, vol. 3, no. 4, pp. 172–180, 2012. View at Google Scholar
  111. W. Prinz, “Perception and Action Planning,” European Journal of Cognitive Psychology, vol. 9, no. 2, pp. 129–154, 1997. View at Publisher · View at Google Scholar · View at Scopus
  112. J. Zhong, M. Peniak, J. Tani, T. Ogata, and A. Cangelosi, “Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs,” Tech. Rep., arXiv, 1605.03261, 2016, arXiv:1605.03261. View at Google Scholar
  113. H. T. Siegelmann and E. D. Sontag, “On the computational power of neural nets,” Journal of Computer and System Sciences, vol. 50, no. 1, pp. 132–150, 1995. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  114. J. Zhong, Artificial Neural Models for Feedback Pathways for Sensorimotor Integration,.
  115. J. Tani, M. Ito, and Y. Sugita, “Self-organization of distributedly represented multiple behavior schemata in a mirror system: Reviews of robot experiments using RNNPB,” Neural Networks, vol. 17, no. 8-9, pp. 1273–1289, 2004. View at Publisher · View at Google Scholar · View at Scopus
  116. W. Hinoshita, H. Arie, J. Tani, H. G. Okuno, and T. Ogata, “Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network,” Neural Networks, vol. 24, no. 4, pp. 311–320, 2011. View at Publisher · View at Google Scholar · View at Scopus
  117. J. Tani, Exploring robotic minds: actions, symbols, and consciousness as self-organizing dynamic phenomena, Oxford University Press, 2016. View at Publisher · View at Google Scholar
  118. A. Ahmadi and J. Tani, “How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? Robotic experiments on imitative interaction,” Neural Networks, vol. 92, pp. 3–16, 2017. View at Publisher · View at Google Scholar · View at Scopus
  119. J. Zhong, A. Cangelosi, and S. Wermter, “Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives,” Frontiers in Behavioral Neuroscience, vol. 8, article no. 22, 2014. View at Publisher · View at Google Scholar · View at Scopus
  120. H. K. Abbas, R. M. Zablotowicz, and H. A. Bruns, “Modeling the colonization of maize by toxigenic and non-toxigenic Aspergillus flavus strains: implications for biological control,” World Mycotoxin Journal, vol. 1, no. 3, pp. 333–340, 2008. View at Publisher · View at Google Scholar
  121. J. Zhong and L. Canamero, “From continuous affective space to continuous expression space: Non-verbal behaviour recognition and generation,” in Proceedings of the 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, IEEE ICDL-EPIROB 2014, pp. 75–80, ita, October 2014. View at Publisher · View at Google Scholar · View at Scopus
  122. J. Zhong, A. Cangelosi, and T. Ogata, “Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple Time-scale Recurrent Models,” in Proceedings of the International Joint Conference on Artificial Neural Networks (IJCNN), 2017.
  123. G. Park and J. Tani, “Development of compositional and contextual communicable congruence in robots by using dynamic neural network models,” Neural Networks, vol. 72, pp. 109–122, 2015. View at Publisher · View at Google Scholar · View at Scopus
  124. A. Ahmadi and J. Tani, “Bridging the gap between probabilistic and deterministic models: a simulation study on a variational Bayes predictive coding recurrent neural network model,” 2017, https://arxiv.org/abs/1706.10240.
  125. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. View at Publisher · View at Google Scholar
  126. J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Networks, vol. 61, pp. 85–117, 2015. View at Publisher · View at Google Scholar · View at Scopus
  127. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS '12), pp. 1097–1105, Lake Tahoe, Nev, USA, December 2012. View at Scopus
  128. W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi, “A survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, pp. 11–26, 2017. View at Publisher · View at Google Scholar · View at Scopus
  129. G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” American Association for the Advancement of Science: Science, vol. 313, no. 5786, pp. 504–507, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  130. B. C. Pijanowski, A. Tayyebi, J. Doucette, B. K. Pekin, D. Braun, and J. Plourde, “A big data urban growth simulation at a national scale: configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing (HPC) environment,” Environmental Modelling & Software, vol. 51, pp. 250–268, 2014. View at Publisher · View at Google Scholar · View at Scopus
  131. D. C. Cireşan, U. Meier, L. M. Gambardella, and J. Schmidhuber, “Deep, big, simple neural nets for handwritten digit recognition,” Neural Computation, vol. 22, no. 12, pp. 3207–3220, 2010. View at Publisher · View at Google Scholar · View at Scopus