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Volumn 14, Issue 3, 2003, Pages 520-533

Trajectory generation and modulation using dynamic neural networks

Author keywords

Dynamic neural network (DNN); Recurrent neural network (RNN); Trajectory generation; Trajectory modulation

Indexed keywords

COMPUTATIONAL COMPLEXITY; CONTROL SYSTEM SYNTHESIS; CONVERGENCE OF NUMERICAL METHODS; DIFFERENTIAL EQUATIONS; FEEDBACK CONTROL; FEEDFORWARD NEURAL NETWORKS; LEARNING SYSTEMS; NONLINEAR EQUATIONS; RELAY CONTROL SYSTEMS; TRAJECTORIES;

EID: 0037507284     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2003.810603     Document Type: Article
Times cited : (33)

References (49)
  • 1
    • 0027699924 scopus 로고
    • Identification and decentralized adaptive control using dynamical neural networks with application to robotic manipulators
    • Nov.
    • A. Karakasoglu, S. I. Sudharsanan, and M. K. Sundareshan, "Identification and decentralized adaptive control using dynamical neural networks with application to robotic manipulators," IEEE Trans. Neural Networks, vol. 4, pp. 919-930, Nov. 1993.
    • (1993) IEEE Trans. Neural Networks , vol.4 , pp. 919-930
    • Karakasoglu, A.1    Sudharsanan, S.I.2    Sundareshan, M.K.3
  • 3
    • 0032074817 scopus 로고    scopus 로고
    • On-line improvement of speed and tracking performance on repetitive paths
    • May
    • F. Boe and B. Hannaford, "On-line improvement of speed and tracking performance on repetitive paths," IEEE Trans. Contr. Syst. Technol., vol. 6, pp. 350-358, May 1998.
    • (1998) IEEE Trans. Contr. Syst. Technol. , vol.6 , pp. 350-358
    • Boe, F.1    Hannaford, B.2
  • 4
    • 0030242079 scopus 로고    scopus 로고
    • An optimal tracking neurocontroller for nonlinear dynamic systems
    • Sept.
    • Y. M. Park, M. S. Choi, and K. Y. Lee, "An optimal tracking neurocontroller for nonlinear dynamic systems," IEEE Trans. Neural Networks, vol. 7, pp. 1099-1110, Sept. 1996.
    • (1996) IEEE Trans. Neural Networks , vol.7 , pp. 1099-1110
    • Park, Y.M.1    Choi, M.S.2    Lee, K.Y.3
  • 6
    • 0024861871 scopus 로고
    • Approximation by superpositions of a sigmoidal function
    • G. Cybenko, "Approximation by superpositions of a sigmoidal function," Math. Contr., Signals, Syst., vol. 2, pp. 303-314, 1989.
    • (1989) Math. Contr., Signals, Syst. , vol.2 , pp. 303-314
    • Cybenko, G.1
  • 7
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal function approximators
    • K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal function approximators," Neural Networks, vol. 2, pp. 359-366, 1999.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 8
    • 0024866495 scopus 로고
    • On the approximate realization of continuous mappings by neural networks
    • K. I. Funahashi, "On the approximate realization of continuous mappings by neural networks," Neural Networks, vol. 2, pp. 183-192, 1989.
    • (1989) Neural Networks , vol.2 , pp. 183-192
    • Funahashi, K.I.1
  • 9
    • 0025056697 scopus 로고
    • Regularization algorithms for learning that are equivalent to multilayer networks
    • T. Poggio and F. Girosi, "Regularization algorithms for learning that are equivalent to multilayer networks," Science, vol. 247, pp. 978-982, 1990.
    • (1990) Science , vol.247 , pp. 978-982
    • Poggio, T.1    Girosi, F.2
  • 11
    • 0022471098 scopus 로고
    • Learning representations of backpropagation errors
    • D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations of backpropagation errors," Nature, vol. 323, pp. 533-536, 1986.
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 13
    • 0031276994 scopus 로고    scopus 로고
    • Fast training of multilayer perceptrons
    • Sept.
    • B. Verma, "Fast training of multilayer perceptrons," IEEE Trans. Neural Networks, vol. 2, pp. 1314-1320, Sept. 1997.
    • (1997) IEEE Trans. Neural Networks , vol.2 , pp. 1314-1320
    • Verma, B.1
  • 14
    • 0033561855 scopus 로고    scopus 로고
    • A fast compact approximation of the exponential function
    • N. N. Schraudolph, "A fast compact approximation of the exponential function," Neural Comput., vol. 11, pp. 853-862, 1999.
    • (1999) Neural Comput. , vol.11 , pp. 853-862
    • Schraudolph, N.N.1
  • 15
    • 0025897859 scopus 로고
    • Neural net algorithms that learn in polynomial time from examples and queries
    • Jan.
    • E. B. Baum, "Neural net algorithms that learn in polynomial time from examples and queries," IEEE Trans. Neural Networks, vol. 2, pp. 5-19, Jan. 1991.
    • (1991) IEEE Trans. Neural Networks , vol.2 , pp. 5-19
    • Baum, E.B.1
  • 16
    • 0027154319 scopus 로고
    • Approximation of dynamical systems by continuous time recurrent neural networks
    • K. I. Funahashi and Y. Nakamura, "Approximation of dynamical systems by continuous time recurrent neural networks," Neural Networks, vol. 6, pp. 801-806, 1993.
    • (1993) Neural Networks , vol.6 , pp. 801-806
    • Funahashi, K.I.1    Nakamura, Y.2
  • 17
    • 0000442791 scopus 로고
    • Generalization of backpropagation to recurrent neural networks
    • Nov.
    • F. J. Pineda, "Generalization of backpropagation to recurrent neural networks," Phys. Rev. Lett., vol. 59, no. 19, pp. 2229-2232, Nov. 1987.
    • (1987) Phys. Rev. Lett. , vol.59 , Issue.19 , pp. 2229-2232
    • Pineda, F.J.1
  • 18
    • 0001202597 scopus 로고
    • Learning state-space trajectories in recurrent neural networks
    • B. A. Pearlmutter, "Learning state-space trajectories in recurrent neural networks," Neural Comput., vol. 1, pp. 263-269, 1989.
    • (1989) Neural Comput. , vol.1 , pp. 263-269
    • Pearlmutter, B.A.1
  • 19
    • 0001202594 scopus 로고
    • A learning algorithm for continually running fully recurrent neural networks
    • R. J. William and D. Zipser, "A learning algorithm for continually running fully recurrent neural networks," Neural Comput., vol. 1, pp. 270-280, 1989.
    • (1989) Neural Comput. , vol.1 , pp. 270-280
    • William, R.J.1    Zipser, D.2
  • 20
    • 0025503558 scopus 로고
    • Backpropagation through time: What it does and how to do it
    • Oct.
    • P. J. Werbos, "Backpropagation through time: What it does and how to do it," Proc. IEEE, vol. 78, pp. 1550-1560, Oct. 1990.
    • (1990) Proc. IEEE , vol.78 , pp. 1550-1560
    • Werbos, P.J.1
  • 21
    • 0001609567 scopus 로고
    • An efficient gradient-based algorithm for on-line training of recurrent network trajectories
    • R. J. Williams and J. Peng, "An efficient gradient-based algorithm for on-line training of recurrent network trajectories," Neural Comput., vol. 2, pp. 490-501, 1990.
    • (1990) Neural Comput. , vol.2 , pp. 490-501
    • Williams, R.J.1    Peng, J.2
  • 22
    • 0029375851 scopus 로고
    • Gradient calculations for dynamic recurrent neural networks: A survey
    • Sept.
    • B. A. Pearlmutter, "Gradient calculations for dynamic recurrent neural networks: A survey," IEEE Trans. Neural Networks, vol. 6, pp. 1212-1228, Sept. 1995.
    • (1995) IEEE Trans. Neural Networks , vol.6 , pp. 1212-1228
    • Pearlmutter, B.A.1
  • 23
    • 0038397468 scopus 로고
    • Faster learning for dynamic recurrent backpropagation
    • Y. Fang and T. J. Sejnowski, "Faster learning for dynamic recurrent backpropagation," Neural Comput., vol. 2, pp. 270-273, 1990.
    • (1990) Neural Comput. , vol.2 , pp. 270-273
    • Fang, Y.1    Sejnowski, T.J.2
  • 24
    • 0026685372 scopus 로고
    • Learning a trajectory using adjoint functions and teacher forcing
    • N. B. Toomarian and J. Barhen, "Learning a trajectory using adjoint functions and teacher forcing," Neural Networks, vol. 5, pp. 473-484, 1992.
    • (1992) Neural Networks , vol.5 , pp. 473-484
    • Toomarian, N.B.1    Barhen, J.2
  • 25
    • 0027309738 scopus 로고
    • A method for improving the real-time recurrent learning algorithm
    • T. Catfolis, "A method for improving the real-time recurrent learning algorithm," Neural Networks, vol. 6, pp. 807-821, 1993.
    • (1993) Neural Networks , vol.6 , pp. 807-821
    • Catfolis, T.1
  • 26
    • 0029079025 scopus 로고
    • Trajectory production with the adaptive time-delay neural network
    • D. T. Lin, J. E. Dayhoff, and P. A. Ligomenides, "Trajectory production with the adaptive time-delay neural network," Neural Networks, vol. 8, no. 3, pp. 447-461, 1995.
    • (1995) Neural Networks , vol.8 , Issue.3 , pp. 447-461
    • Lin, D.T.1    Dayhoff, J.E.2    Ligomenides, P.A.3
  • 27
    • 0028400379 scopus 로고
    • Recurrent neural network training with feedforward complexity
    • Mar.
    • O. Olurotimi, "Recurrent neural network training with feedforward complexity," IEEE Trans. Neural Networks, vol. 5, pp. 185-197, Mar. 1994.
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 185-197
    • Olurotimi, O.1
  • 28
    • 0031037681 scopus 로고    scopus 로고
    • Efficient training of recurrent neural networks with time delays
    • B. Cohen, D. Saad, and E. Marom, "Efficient training of recurrent neural networks with time delays," Neural Networks, vol. 10, no. 1, pp. 51-59, 1997.
    • (1997) Neural Networks , vol.10 , Issue.1 , pp. 51-59
    • Cohen, B.1    Saad, D.2    Marom, E.3
  • 29
    • 0032664889 scopus 로고    scopus 로고
    • Learning continuous trajectories in recurrent neural networks with time dependent weights
    • July
    • M. Galicki, L. Leistritz, and H. White, "Learning continuous trajectories in recurrent neural networks with time dependent weights," IEEE Trans. Neural Networks, vol. 10, pp. 741-756, July 1999.
    • (1999) IEEE Trans. Neural Networks , vol.10 , pp. 741-756
    • Galicki, M.1    Leistritz, L.2    White, H.3
  • 30
    • 0028399791 scopus 로고
    • On the problem of local minima in recurrent neural networks
    • Mar.
    • M. Bianchini, M. Gori, and M. Maggini, "On the problem of local minima in recurrent neural networks," IEEE Trans. Neural Networks, vol. 5, pp. 167-177, Mar. 1994.
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 167-177
    • Bianchini, M.1    Gori, M.2    Maggini, M.3
  • 31
    • 0028392483 scopus 로고
    • Learning long term dependencies with gradient descent in difficult
    • Mar.
    • Y. Bengio, P. Simard, and P. Frasconi, "Learning long term dependencies with gradient descent in difficult," IEEE Trans. Neural Networks, vol. 5, pp. 157-166, Mar. 1994.
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 157-166
    • Bengio, Y.1    Simard, P.2    Frasconi, P.3
  • 32
    • 0028202641 scopus 로고
    • An evolutionary algorithm that constructs recurrent neural networks
    • Jan.
    • P. J. Angeline, G. M. Saunders, and J. B. Pollack, "An evolutionary algorithm that constructs recurrent neural networks," IEEE Trans. Neural Networks, vol. 5, pp. 54-65, Jan. 1994.
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 54-65
    • Angeline, P.J.1    Saunders, G.M.2    Pollack, J.B.3
  • 33
    • 0032072476 scopus 로고    scopus 로고
    • Recurrent neural network training by a learning automation approach for trajectory learning and control system design
    • Jan.
    • M. K. Sundareshan and T. A. Condarcure, "Recurrent neural network training by a learning automation approach for trajectory learning and control system design," IEEE Trans. Neural Networks, vol. 9, pp. 1-15, Jan. 1998.
    • (1998) IEEE Trans. Neural Networks , vol.9 , pp. 1-15
    • Sundareshan, M.K.1    Condarcure, T.A.2
  • 34
    • 0038059064 scopus 로고    scopus 로고
    • Patterns of dynamic activity and timing in neural network processing
    • O. Omidvar and J. Dayhoff, Eds. New York: Academic
    • J. E. Dayhoff, P. J. Palmadesso, F. Richards, and D. T. Lin, "Patterns of dynamic activity and timing in neural network processing," in Neural Networks and Pattern Recognition, O. Omidvar and J. Dayhoff, Eds. New York: Academic, 1998.
    • (1998) Neural Networks and Pattern Recognition
    • Dayhoff, J.E.1    Palmadesso, P.J.2    Richards, F.3    Lin, D.T.4
  • 35
    • 0038059062 scopus 로고    scopus 로고
    • Training algorithms for recurrent neural nets that eliminate the need for computation of error gradients with applications to trajectory production problems
    • L. Medsker and L. C. Jain, Eds. Boca Raton, FL: CRC
    • M. K. Sundareshan, Y. C. Wong, and T. Condarcure, "Training algorithms for recurrent neural nets that eliminate the need for computation of error gradients with applications to trajectory production problems," in Recurrent Neural Networks: Design and Applications, L. Medsker and L. C. Jain, Eds. Boca Raton, FL: CRC, 1999.
    • (1999) Recurrent Neural Networks: Design and Applications
    • Sundareshan, M.K.1    Wong, Y.C.2    Condarcure, T.3
  • 36
    • 0032121570 scopus 로고    scopus 로고
    • Existence, learning and replication of periodic motions in recurrent neural networks
    • July
    • A. Ruiz, D. H. Owens, and S. Townley, "Existence, learning and replication of periodic motions in recurrent neural networks," IEEE Trans. Neural Networks, vol. 9, pp. 651-661, July 1998.
    • (1998) IEEE Trans. Neural Networks , vol.9 , pp. 651-661
    • Ruiz, A.1    Owens, D.H.2    Townley, S.3
  • 38
    • 0000909827 scopus 로고
    • Neurons with graded response have collective computational properties like those of two-state neurons
    • J. J. Hopfield, "Neurons with graded response have collective computational properties like those of two-state neurons," Proc. Nat. Academy Sci. USA, vol. 79, pp. 2554-2558, 1984.
    • (1984) Proc. Nat. Academy Sci. USA , vol.79 , pp. 2554-2558
    • Hopfield, J.J.1
  • 39
    • 0028506509 scopus 로고
    • Supervised training of dynamical neural networks for associative memory design and identification of nonlinear maps
    • Sept.
    • S. I. Sudharsanan and M. K. Sundareshan, "Supervised training of dynamical neural networks for associative memory design and identification of nonlinear maps," Int. J. Neural Syst., vol. 5, no. 3, pp. 165-180, Sept. 1994.
    • (1994) Int. J. Neural Syst. , vol.5 , Issue.3 , pp. 165-180
    • Sudharsanan, S.I.1    Sundareshan, M.K.2
  • 40
    • 0033703459 scopus 로고    scopus 로고
    • Periodic motions, mapping ordered sequences and training dynamic neural networks to generate continuous and discontinuous trajectories
    • P. Zegers and M. K. Sundareshan, "Periodic motions, mapping ordered sequences and training dynamic neural networks to generate continuous and discontinuous trajectories," in Proc. Int. Joint Conf. Neural Networks, Session WA-1, Como, Italy, 2000, pp. 619-625.
    • Proc. Int. Joint Conf. Neural Networks, Session WA-1, Como, Italy, 2000 , pp. 619-625
    • Zegers, P.1    Sundareshan, M.K.2
  • 41
  • 43
    • 0021497175 scopus 로고
    • Automatic tuning of simple regulators with specifications on phase and amplitude margins
    • K. J. AstromHagglund, "Automatic tuning of simple regulators with specifications on phase and amplitude margins," Automatica, vol. 20, no. 5, pp. 645-651, 1984.
    • (1984) Automatica , vol.20 , Issue.5 , pp. 645-651
    • AstromHagglund, K.J.1
  • 45
    • 0003792312 scopus 로고
    • Englewood Cliffs, NJ: Prentice-Hall
    • T. Kailath, Linear Systems. Englewood Cliffs, NJ: Prentice-Hall, 1980.
    • (1980) Linear Systems
    • Kailath, T.1
  • 47
    • 0038735488 scopus 로고    scopus 로고
    • Comparison of recurrent neural networks for trajectory generation
    • L. Medsker and L. C. Jain, Eds. Boca Raton, FL: CRC
    • D. G. Hagner, M. H. Hassoun, and P. B. Watta, "Comparison of recurrent neural networks for trajectory generation," in Recurrent Neural Networks: Design and Applications, L. Medsker and L. C. Jain, Eds. Boca Raton, FL: CRC, 1999.
    • (1999) Recurrent Neural Networks: Design and Applications
    • Hagner, D.G.1    Hassoun, M.H.2    Watta, P.B.3
  • 49
    • 4244118084 scopus 로고    scopus 로고
    • Some new results on the architecture training process and estimation error bounds for learning machines
    • Ph.D. dissertation, Univ. Arizona
    • P. Zegers, "Some new results on the architecture training process and estimation error bounds for learning machines," Ph.D. dissertation, Univ. Arizona, 2002.
    • (2002)
    • Zegers, P.1


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