메뉴 건너뛰기




Volumn , Issue , 2007, Pages 117-139

Water reservoirs management under uncertainty by approximating networks and learning from data

Author keywords

[No Author keywords available]

Indexed keywords


EID: 84882555334     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-008044967-8/50006-3     Document Type: Chapter
Times cited : (11)

References (45)
  • 1
    • 12444304456 scopus 로고    scopus 로고
    • Optimization of approximating networks for optimal fault diagnosis
    • Alessandri A., Sanguineti M. Optimization of approximating networks for optimal fault diagnosis. Optim. Method. Softw. 2005, 20:235-260.
    • (2005) Optim. Method. Softw. , vol.20 , pp. 235-260
    • Alessandri, A.1    Sanguineti, M.2
  • 2
    • 84882506277 scopus 로고    scopus 로고
    • Sanguineti (to appear). A recursive algorithm for nonlinear least-squares problems. Comput. Optim. Appl.
    • Alessandri, A., M. Cuneo, S. Pagnan and M. Sanguineti (to appear). A recursive algorithm for nonlinear least-squares problems. Comput. Optim. Appl.
    • Alessandri, A.M.1    Cuneo, S.2    Pagnan, M.3
  • 3
    • 0036506049 scopus 로고    scopus 로고
    • Optimization-based learning with bounded error for feedforward neural networks
    • Alessandri A., Sanguineti M., Maggiore M. Optimization-based learning with bounded error for feedforward neural networks. IEEE T. Neural Networ. 2002, 13:261-273.
    • (2002) IEEE T. Neural Networ. , vol.13 , pp. 261-273
    • Alessandri, A.1    Sanguineti, M.2    Maggiore, M.3
  • 4
    • 0030618794 scopus 로고    scopus 로고
    • An aggregate stochastic dynamic programming model of multireservoir systems
    • Archibald T.W., McKinnon K.I.M., Thomas L.C. An aggregate stochastic dynamic programming model of multireservoir systems. Water Resour. Res. 1997, 33:333-340.
    • (1997) Water Resour. Res. , vol.33 , pp. 333-340
    • Archibald, T.W.1    McKinnon, K.I.M.2    Thomas, L.C.3
  • 5
    • 18744415637 scopus 로고    scopus 로고
    • Facing the curse of dimensionality by the extended Ritz method in stochastic functional optimization: dynamic routing in traffic networks
    • Kluwer Academic Publishers, G. Di Pillo, A. Murli (Eds.)
    • Baglietto M., Sanguineti M., Zoppoli R. Facing the curse of dimensionality by the extended Ritz method in stochastic functional optimization: dynamic routing in traffic networks. High Performance Algorithms and Software for Nonlinear Optimization 2003, 23-56. Kluwer Academic Publishers. G. Di Pillo, A. Murli (Eds.).
    • (2003) High Performance Algorithms and Software for Nonlinear Optimization , pp. 23-56
    • Baglietto, M.1    Sanguineti, M.2    Zoppoli, R.3
  • 6
    • 0035330108 scopus 로고    scopus 로고
    • Distributed-information neural control: the case of dynamic routing in traffic networks
    • Baglietto M., Parisini T., Zoppoli R. Distributed-information neural control: the case of dynamic routing in traffic networks. IEEE T. Neural Networ. 2001, 12:485-502.
    • (2001) IEEE T. Neural Networ. , vol.12 , pp. 485-502
    • Baglietto, M.1    Parisini, T.2    Zoppoli, R.3
  • 7
    • 0027599793 scopus 로고
    • Universal approximation bounds for superpositions of a sigmoidal function
    • Barron A.R. Universal approximation bounds for superpositions of a sigmoidal function. IEEE T. Inform. Theory 1993, 39:930-945.
    • (1993) IEEE T. Inform. Theory , vol.39 , pp. 930-945
    • Barron, A.R.1
  • 8
    • 0001325515 scopus 로고
    • Approximation and estimation bounds for artificial neural networks
    • Barron A.R. Approximation and estimation bounds for artificial neural networks. Mach. Learn. 1994, 14:115-133.
    • (1994) Mach. Learn. , vol.14 , pp. 115-133
    • Barron, A.R.1
  • 9
    • 0003787146 scopus 로고
    • Princeton University Press, Princeton, NJ
    • Bellman R. Dynamic Programming 1957, Princeton University Press, Princeton, NJ.
    • (1957) Dynamic Programming
    • Bellman, R.1
  • 10
    • 84968468700 scopus 로고
    • Polynomial approximation-a new computational technique in dynamic programming
    • Bellman R., Kalaba R., Kotkin B. Polynomial approximation-a new computational technique in dynamic programming. Math. Comput. 1963, 17:155-161.
    • (1963) Math. Comput. , vol.17 , pp. 155-161
    • Bellman, R.1    Kalaba, R.2    Kotkin, B.3
  • 12
    • 2542562008 scopus 로고    scopus 로고
    • Deterministic design for neural network learning: An ap-proach based on discrepancy
    • Cervellera C., Muselli M. Deterministic design for neural network learning: An ap-proach based on discrepancy. IEEE T. Neural Networ. 2004, 15:533-544.
    • (2004) IEEE T. Neural Networ. , vol.15 , pp. 533-544
    • Cervellera, C.1    Muselli, M.2
  • 13
    • 0001820934 scopus 로고    scopus 로고
    • Applying experimental design and regres-sion splines to high-dimensional continuous-state stochastic dynamic programming
    • Chen V.C.P., Ruppert D., Shoemaker C.A. Applying experimental design and regres-sion splines to high-dimensional continuous-state stochastic dynamic programming. Oper. Res. 1999, 47:38-53.
    • (1999) Oper. Res. , vol.47 , pp. 38-53
    • Chen, V.C.P.1    Ruppert, D.2    Shoemaker, C.A.3
  • 15
    • 0024158874 scopus 로고
    • Gradient dynamic programming for stochastic optimal control of multidimensional water resources systems
    • Foufoula-Georgiou E., Kitanidis P.K. Gradient dynamic programming for stochastic optimal control of multidimensional water resources systems. Water Resour. Res. 1988, 24:1345-1359.
    • (1988) Water Resour. Res. , vol.24 , pp. 1345-1359
    • Foufoula-Georgiou, E.1    Kitanidis, P.K.2
  • 17
    • 0001219859 scopus 로고
    • Regularization theory and neural networks architectures
    • Girosi F., Jones M., Poggio T. Regularization theory and neural networks architectures. Neural Comput. 1995, 7:219-269.
    • (1995) Neural Comput. , vol.7 , pp. 219-269
    • Girosi, F.1    Jones, M.2    Poggio, T.3
  • 18
    • 84882552658 scopus 로고    scopus 로고
    • Zoppoli (to appear). Approximation schemes for functional optimization problems. Journal of Optimization Theory and Applicat
    • Giulini, S., M. Sanguineti and R. Zoppoli (to appear). Approximation schemes for functional optimization problems. Journal of Optimization Theory and Applicat.
    • Giulini, S.M.1    Sanguineti, R.2
  • 19
    • 0001546764 scopus 로고    scopus 로고
    • Convergent on-line algorithms for supervised learning in neural networks
    • Grippo L. Convergent on-line algorithms for supervised learning in neural networks. IEEE T. Neural Networ. 2000, 11:1284-1299.
    • (2000) IEEE T. Neural Networ. , vol.11 , pp. 1284-1299
    • Grippo, L.1
  • 21
    • 0027601994 scopus 로고
    • Numerical solution of continuous-state dynamic programs using linear and spline interpolation
    • Johnson S.A., Stedinger J.R., Shoemaker C., Li Y, Tejada-Guibert J.A. Numerical solution of continuous-state dynamic programs using linear and spline interpolation. Oper. Res. 1993, 41:484-500.
    • (1993) Oper. Res. , vol.41 , pp. 484-500
    • Johnson, S.A.1    Stedinger, J.R.2    Shoemaker, C.3    Li, Y.4    Tejada-Guibert, J.A.5
  • 22
    • 2542498732 scopus 로고    scopus 로고
    • Minimization of error functionals over variable-basis functions
    • Kainen P.C., Kurková V., Sanguineti M. Minimization of error functionals over variable-basis functions. SIAM J. Optimiz. 2003, 14:732-742.
    • (2003) SIAM J. Optimiz. , vol.14 , pp. 732-742
    • Kainen, P.C.1    Kurková, V.2    Sanguineti, M.3
  • 23
    • 0035443484 scopus 로고    scopus 로고
    • Bounds on rates of variable-basis and neural-network ap-proximation
    • Kurková V., Sanguineti M. Bounds on rates of variable-basis and neural-network ap-proximation. IEEE T Inform. Theory 2001, 47:2659-2665.
    • (2001) IEEE T Inform. Theory , vol.47 , pp. 2659-2665
    • Kurková, V.1    Sanguineti, M.2
  • 24
    • 0036165028 scopus 로고    scopus 로고
    • Comparison of worst case errors in linear and neural net-work approximation
    • Kurková V., Sanguineti M. Comparison of worst case errors in linear and neural net-work approximation. IEEE T. Inform. Theory 2002, 48:264-275.
    • (2002) IEEE T. Inform. Theory , vol.48 , pp. 264-275
    • Kurková, V.1    Sanguineti, M.2
  • 25
    • 18744392546 scopus 로고    scopus 로고
    • Error estimates for approximate optimization by the ex-tended Ritz method
    • Kurková V., Sanguineti M. Error estimates for approximate optimization by the ex-tended Ritz method. SIAM J. Optimiz. 2005, 15:461-487.
    • (2005) SIAM J. Optimiz. , vol.15 , pp. 461-487
    • Kurková, V.1    Sanguineti, M.2
  • 26
    • 18144390163 scopus 로고    scopus 로고
    • Learning with generalization capability by kernel methods of bounded complexity
    • Kurková V., Sanguineti M. Learning with generalization capability by kernel methods of bounded complexity. J. Complexity 2005, 21:350-367.
    • (2005) J. Complexity , vol.21 , pp. 350-367
    • Kurková, V.1    Sanguineti, M.2
  • 29
    • 0027262895 scopus 로고
    • Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
    • Leshno M., Ya V., Pinkus A., Schocken S. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Networks 1993, 6:861-867.
    • (1993) Neural Networks , vol.6 , pp. 861-867
    • Leshno, M.1    Ya, V.2    Pinkus, A.3    Schocken, S.4
  • 31
    • 0000482137 scopus 로고    scopus 로고
    • On the relationship between generalization error, hypothesis com-plexity, and sample complexity for radial basis functions
    • Niyogi P., Girosi F. On the relationship between generalization error, hypothesis com-plexity, and sample complexity for radial basis functions. Neural Comput. 1996, 8:819-842.
    • (1996) Neural Comput. , vol.8 , pp. 819-842
    • Niyogi, P.1    Girosi, F.2
  • 32
    • 0039224179 scopus 로고
    • On nonparametric estimation of a regression function that is smooth in a domain on
    • Nussbaum M. On nonparametric estimation of a regression function that is smooth in a domain on. Rk. Theor. Probab. Appl. 1986, 31:118-125.
    • (1986) Rk. Theor. Probab. Appl. , vol.31 , pp. 118-125
    • Nussbaum, M.1
  • 33
    • 0028425062 scopus 로고
    • Neural networks for feedback feedforward nonlinear control systems
    • Parisini T., Zoppoli R. Neural networks for feedback feedforward nonlinear control systems. IEEE T. Neural Networ. 1994, 5:436-449.
    • (1994) IEEE T. Neural Networ. , vol.5 , pp. 436-449
    • Parisini, T.1    Zoppoli, R.2
  • 34
    • 0030169366 scopus 로고    scopus 로고
    • Neural approximations for multistage optimal control of non-linear stochastic systems
    • Parisini T., Zoppoli R. Neural approximations for multistage optimal control of non-linear stochastic systems. IEEE T. Automat. Contr. 1996, 41:889-895.
    • (1996) IEEE T. Automat. Contr. , vol.41 , pp. 889-895
    • Parisini, T.1    Zoppoli, R.2
  • 35
    • 0032209114 scopus 로고    scopus 로고
    • Neural approximations for infinite-horizon optimal control of nonlinear stochastic systems
    • Parisini T., Zoppoli R. Neural approximations for infinite-horizon optimal control of nonlinear stochastic systems. IEEE T. Neural Networ. 1998, 9:1388-1408.
    • (1998) IEEE T. Neural Networ. , vol.9 , pp. 1388-1408
    • Parisini, T.1    Zoppoli, R.2
  • 36
    • 0000106040 scopus 로고
    • Universal approximation using radial-basis-function networks
    • Park J., Sandberg I.W. Universal approximation using radial-basis-function networks. Neural Comput. 1991, 3:246-257.
    • (1991) Neural Comput. , vol.3 , pp. 246-257
    • Park, J.1    Sandberg, I.W.2
  • 37
    • 0035328580 scopus 로고    scopus 로고
    • Improved dynamic programming methods for optimal control of lumped-parameter stochastic systems
    • Philbrick C.R., Kitanidis P.K. Improved dynamic programming methods for optimal control of lumped-parameter stochastic systems. Oper. Res. 1999, 49:398-412.
    • (1999) Oper. Res. , vol.49 , pp. 398-412
    • Philbrick, C.R.1    Kitanidis, P.K.2
  • 38
    • 0001352321 scopus 로고
    • The distribution of points in a cube and the approximate evaluation of integrals
    • Sobol' I.M. The distribution of points in a cube and the approximate evaluation of integrals. Zh. Vychisl. Mat. i Mat. Fiz. 1967, 7:784-802.
    • (1967) Zh. Vychisl. Mat. i Mat. Fiz. , vol.7 , pp. 784-802
    • Sobol', I.M.1
  • 40
    • 0019716203 scopus 로고
    • A decomposition method for the long-term scheduling of reservoirs in series
    • Turgeon A. A decomposition method for the long-term scheduling of reservoirs in series. Water Resour. Res. 1981, 17:1565-1570.
    • (1981) Water Resour. Res. , vol.17 , pp. 1565-1570
    • Turgeon, A.1
  • 42
    • 0020335699 scopus 로고
    • Dynamic programming applications in water resources
    • Yakowitz S. Dynamic programming applications in water resources. Water Resour. Res. 1982, 18:673-696.
    • (1982) Water Resour. Res. , vol.18 , pp. 673-696
    • Yakowitz, S.1
  • 43
    • 0003320059 scopus 로고
    • Learning techniques and neural networks for the solution of n-stage nonlinear nonquadratic optimal control problems
    • Birkhäuser, Boston, A. Isidori, T.J. Tarn (Eds.)
    • Zoppoli R., Parisini T. Learning techniques and neural networks for the solution of n-stage nonlinear nonquadratic optimal control problems. Systems, Models and Feedback: Theory and Applications 1992, 193-210. Birkhäuser, Boston. A. Isidori, T.J. Tarn (Eds.).
    • (1992) Systems, Models and Feedback: Theory and Applications , pp. 193-210
    • Zoppoli, R.1    Parisini, T.2
  • 44
    • 0036115636 scopus 로고    scopus 로고
    • Approximating networks and extended Ritz method for the solution of functional optimization problems
    • Zoppoli R., Parisini T., Sanguineti M. Approximating networks and extended Ritz method for the solution of functional optimization problems. J. Optimiz. Theory Appl. 2001, 112:403-440.
    • (2001) J. Optimiz. Theory Appl. , vol.112 , pp. 403-440
    • Zoppoli, R.1    Parisini, T.2    Sanguineti, M.3
  • 45
    • 84882505428 scopus 로고    scopus 로고
    • Parisini (to appear). Neural Approximations for Optimal Control and Decision. Springer-Verlag, London, "Cont Communications Systems Series"
    • Zoppoli, R., M. Sanguineti, M. Baglietto and T. Parisini (to appear). Neural Approximations for Optimal Control and Decision. Springer-Verlag, London, "Cont Communications Systems Series".
    • Zoppoli, R.M.1    Sanguineti, M.2    Baglietto, T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.