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Volumn , Issue , 2007, Pages 268-271

Toward effective combination of off-line and on-line training in ADP framework

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; KALMAN FILTERS; REAL TIME SYSTEMS; RECURRENT NEURAL NETWORKS; REINFORCEMENT LEARNING; ROBUSTNESS (CONTROL SYSTEMS);

EID: 34548777734     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ADPRL.2007.368198     Document Type: Conference Paper
Times cited : (12)

References (16)
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  • 2
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    • D. V. Prokhorov, G. V. Puskorius, and L. A. Feldkamp, "Dynamical neural networks for control," see in A Field Guide to Dynamical Recurrent Networks, J. Kolen and S. Kremer (Eds.), IEEE Press, 2001, pp. 257-289.
    • (2001) A Field Guide to Dynamical Recurrent Networks , pp. 257-289
  • 3
    • 34547129948 scopus 로고    scopus 로고
    • Training Recurrent Neurocontrollers for Robustness with Derivative-Free Kalman Filter
    • November
    • D. Prokhorov, "Training Recurrent Neurocontrollers for Robustness with Derivative-Free Kalman Filter," IEEE Trans. Neural Networks, November 2006, pp. 1606-1616.
    • (2006) IEEE Trans. Neural Networks , pp. 1606-1616
    • Prokhorov, D.1
  • 4
    • 34547133026 scopus 로고    scopus 로고
    • Training Recurrent Neurocontrollers for Real-Time Applications
    • to appear
    • D. Prokhorov, "Training Recurrent Neurocontrollers for Real-Time Applications," IEEE Trans. Neural Networks, to appear.
    • IEEE Trans. Neural Networks
    • Prokhorov, D.1
  • 5
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    • Hierarchical neural network model for voluntary movement with application to robotics
    • April
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    • (1988) IEEE Control Systems Magazine , vol.8 , Issue.2 , pp. 8-15
    • Kawato, M.1    Uno, Y.2    Isobe, M.3    Suzuki, R.4
  • 7
    • 34548733572 scopus 로고    scopus 로고
    • Publications of Paul J. Werbos at www.werbos.com.
    • Publications of Paul J. Werbos at www.werbos.com.
  • 8
    • 0012331016 scopus 로고
    • Memory Approaches To Reinforcement Learning In Non-Markovian Domains
    • Technical Report CMU-CS-92-138, School of Computer Science, Carnegie Mellon University, May
    • L.-J. Lin and T. Mitchell, Memory Approaches To Reinforcement Learning In Non-Markovian Domains, Technical Report CMU-CS-92-138, School of Computer Science, Carnegie Mellon University, May 1992.
    • (1992)
    • Lin, L.-J.1    Mitchell, T.2
  • 9
    • 0033750123 scopus 로고    scopus 로고
    • Neurocontroller Alternatives for Fuzzy Ball-and-Beam Systems with Nonlinear, Nonuniform Friction
    • March
    • P. Eaton, D. Prokhorov, and D. Wunsch, "Neurocontroller Alternatives for Fuzzy Ball-and-Beam Systems with Nonlinear, Nonuniform Friction," IEEE Trans. on Neural Networks, March 2000, pp. 423-435.
    • (2000) IEEE Trans. on Neural Networks , pp. 423-435
    • Eaton, P.1    Prokhorov, D.2    Wunsch, D.3
  • 10
    • 84954240138 scopus 로고    scopus 로고
    • Modeling Reward Functions for Incomplete State Representations via Echo State Networks
    • Montreal, Canada, August 1-4
    • K. Bush and C. Anderson, "Modeling Reward Functions for Incomplete State Representations via Echo State Networks," Proceedings of the International Joint Conference on Neural Networks, Montreal, Canada, August 1-4, 2005.
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  • 13
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  • 14
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.