메뉴 건너뛰기




Volumn 13, Issue 4, 2004, Pages 339-351

On three intelligent systems: Dynamic neural, fuzzy, and wavelet networks for training trajectory

Author keywords

Adjoint theory; Dynamic networks; Fuzzy systems; Intelligent systems; Neural network; Training trajectory; Wavelets

Indexed keywords

ALGORITHMS; COGNITIVE SYSTEMS; CONTROL SYSTEM ANALYSIS; FUZZY CONTROL; MATHEMATICAL MODELS; NEURAL NETWORKS; ROBUSTNESS (CONTROL SYSTEMS); WAVELET TRANSFORMS;

EID: 11144305054     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-004-0429-9     Document Type: Conference Paper
Times cited : (24)

References (75)
  • 1
    • 0344431460 scopus 로고    scopus 로고
    • PhD thesis, Sakarya University, Sakarya, Turkey
    • Becerikli Y (1998) Neuro-optimal control. PhD thesis, Sakarya University, Sakarya, Turkey
    • (1998) Neuro-optimal Control
    • Becerikli, Y.1
  • 3
    • 0037335434 scopus 로고    scopus 로고
    • Intelligent optimal control with dynamic neural networks
    • Becerikli Y, Konar AF, Samad T (2003) Intelligent optimal control with dynamic neural networks. Neural Netw 16(2):251259
    • (2003) Neural Netw , vol.16 , Issue.2 , pp. 251-259
    • Becerikli, Y.1    Konar, A.F.2    Samad, T.3
  • 4
    • 2442499479 scopus 로고    scopus 로고
    • Trajectory priming with dynamic fuzzy networks in nonlinear optimal control
    • Becerikli Y, Oysal Y, Konar AF (2004) Trajectory priming with dynamic fuzzy networks in nonlinear optimal control. IEEE Trans Neural Net 15(2):383-394
    • (2004) IEEE Trans Neural Net , vol.15 , Issue.2 , pp. 383-394
    • Becerikli, Y.1    Oysal, Y.2    Konar, A.F.3
  • 7
    • 0029411990 scopus 로고
    • Space-frequency localized basis function networks for nonlinear system estimation and control
    • Cannon M, Slotine J-JE (1995) Space-frequency localized basis function networks for nonlinear system estimation and control. Neurocomputing 9(3):293-342
    • (1995) Neurocomputing , vol.9 , Issue.3 , pp. 293-342
    • Cannon, M.1    Slotine, J.-J.E.2
  • 8
    • 0000177222 scopus 로고
    • Identification of dynamic fuzzy model
    • Cao SG, Rees NW (1995) Identification of dynamic fuzzy model. Fuzzy Set Syst 74(3):307-320
    • (1995) Fuzzy Set Syst , vol.74 , Issue.3 , pp. 307-320
    • Cao, S.G.1    Rees, N.W.2
  • 11
    • 0024861871 scopus 로고
    • Approximation by superpositions of a sigmoidal function
    • Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signal 2:303-314
    • (1989) Math Control Signal , vol.2 , pp. 303-314
    • Cybenko, G.1
  • 12
    • 0030242952 scopus 로고    scopus 로고
    • Wavelet transform based adaptive filters: Analysis and new results
    • Erdol N, Basbug F (1996) Wavelet transform based adaptive filters: analysis and new results. IEEE Trans Signal Process 44(9):2163-2169
    • (1996) IEEE Trans Signal Process , vol.44 , Issue.9 , pp. 2163-2169
    • Erdol, N.1    Basbug, F.2
  • 13
    • 0026899732 scopus 로고
    • A constructive method for multivariate function approximation by multilayer perceptrons
    • Geva S, Sitte J (1992) A constructive method for multivariate function approximation by multilayer perceptrons. IEEE Trans Neural Netw 3(4):621-624
    • (1992) IEEE Trans Neural Netw , vol.3 , Issue.4 , pp. 621-624
    • Geva, S.1    Sitte, J.2
  • 14
    • 3342925450 scopus 로고
    • An overview of artificial intelligence and robotics
    • Gevarter WB (1983) An overview of artificial intelligence and robotics, vol I. NASA technical memorandum 85836
    • (1983) NASA Technical Memorandum 85836 , vol.1
    • Gevarter, W.B.1
  • 17
    • 0020118274 scopus 로고
    • Neural networks and physical systems with emergent collective computational abilities
    • Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci USA 79:2554-2558
    • (1982) Proc Nat Acad Sci USA , vol.79 , pp. 2554-2558
    • Hopfield, J.J.1
  • 18
    • 0004469897 scopus 로고
    • Neurons with graded response have collective computational properties like those of two-state neurons
    • Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Nat Acad Sci USA 81:3088-3092
    • (1984) Proc Nat Acad Sci USA , vol.81 , pp. 3088-3092
    • Hopfield, J.J.1
  • 19
    • 0024880831 scopus 로고
    • Multilayer feed-forward networks are universal approximators
    • Hornik K, Stinchcombe M, White H (1985) Multilayer feed-forward networks are universal approximators. Neural Netw 2:359-366
    • (1985) Neural Netw , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 20
    • 0001307541 scopus 로고
    • Degree of approximation results for feedforward networks approximating unknown mappings and their derivatives
    • Hornik K, Stinchcombe M, White H, Auer P (1994) Degree of approximation results for feedforward networks approximating unknown mappings and their derivatives. Neural Comput 6(6):1262-1275
    • (1994) Neural Comput , vol.6 , Issue.6 , pp. 1262-1275
    • Hornik, K.1    Stinchcombe, M.2    White, H.3    Auer, P.4
  • 22
    • 0026384304 scopus 로고
    • Robust adaptive control: A unified approach
    • Iannou PA, Datta A (1991) Robust adaptive control: a unified approach. Proc IEEE 79(12):1736-1768
    • (1991) Proc IEEE , vol.79 , Issue.12 , pp. 1736-1768
    • Iannou, P.A.1    Datta, A.2
  • 24
    • 0001887517 scopus 로고
    • Attractor dynamics and parallelism in connectionist sequential machine
    • Amherst, Massachusetts. Lawrence Erlbaum, Hillsdale, New Jersey
    • Jordan MI (1986) Attractor dynamics and parallelism in connectionist sequential machine. In: Proceedings of the 8th annual conference of the cognitive science society, Amherst, Massachusetts. Lawrence Erlbaum, Hillsdale, New Jersey
    • (1986) Proceedings of the 8th Annual Conference of the Cognitive Science Society
    • Jordan, M.I.1
  • 25
    • 0004178386 scopus 로고    scopus 로고
    • Prentice-Hall, Englewood Cliffs, New Jersey
    • Khalil HK (1996) Nonlinear systems. Prentice-Hall, Englewood Cliffs, New Jersey
    • (1996) Nonlinear Systems
    • Khalil, H.K.1
  • 27
    • 11144269828 scopus 로고
    • Gradient and curvature in nonlinear identification
    • Minneapolis, Minnesota, January 1991
    • Konar AF (1991) Gradient and curvature in nonlinear identification. In: Proceedings of the Honeywell advanced control workshop, Minneapolis, Minnesota, January 1991
    • (1991) Proceedings of the Honeywell Advanced Control Workshop
    • Konar, A.F.1
  • 29
    • 0142135102 scopus 로고
    • Hybrid neural network/algorithmic system identification
    • Honeywell Technology Center, 3660 Technology Drive, Minneapolis, MN 55418
    • Konar AF, Samad T (1991) Hybrid neural network/algorithmic system identification. Technical report SSDC-91-I 4051-1, Honeywell Technology Center, 3660 Technology Drive, Minneapolis, MN 55418
    • (1991) Technical Report , vol.SSDC-91-I 4051-1
    • Konar, A.F.1    Samad, T.2
  • 30
    • 0142071770 scopus 로고
    • Dynamic neural networks
    • Honeywell Technology Center, 3660 Technology Drive, Minneapolis, MN 55418
    • Konar AF, Samad T (1992) Dynamic neural networks. Technical report SSDC-92-I 4152-2, Honeywell Technology Center, 3660 Technology Drive, Minneapolis, MN 55418
    • (1992) Technical Report , vol.SSDC-92-I 4152-2
    • Konar, A.F.1    Samad, T.2
  • 32
    • 0004262910 scopus 로고    scopus 로고
    • Prentice-Hall, Upper Saddle River, New Jersey
    • Kosko B (1997) Fuzzy engineering. Prentice-Hall, Upper Saddle River, New Jersey
    • (1997) Fuzzy Engineering
    • Kosko, B.1
  • 33
    • 0029264243 scopus 로고
    • High-order neural network structures for identification of dynamical systems
    • Kosmatopoulos EB, Polycarpas MM, Christodoulou MA, Iannou PA (1995) High-order neural network structures for identification of dynamical systems. IEEE Trans Neural Netw 6(2):431-442
    • (1995) IEEE Trans Neural Netw , vol.6 , Issue.2 , pp. 431-442
    • Kosmatopoulos, E.B.1    Polycarpas, M.M.2    Ma, C.3    Iannou, P.A.4
  • 35
    • 0036506072 scopus 로고    scopus 로고
    • Training trajectories by continuous recurrent multilayer networks
    • Leistritz L, Galicki M, Witte H, Kochs E (2002) Training trajectories by continuous recurrent multilayer networks. IEEE Trans Neural Netw 13(2):283-291
    • (2002) IEEE Trans Neural Netw , vol.13 , Issue.2 , pp. 283-291
    • Leistritz, L.1    Galicki, M.2    Witte, H.3    Kochs, E.4
  • 36
    • 0026135904 scopus 로고
    • Radial basis function networks for classifying process faults
    • Leonard J, Kramer M (1991) Radial basis function networks for classifying process faults. IEEE Control Syst 11:31-38
    • (1991) IEEE Control Syst , vol.11 , pp. 31-38
    • Leonard, J.1    Kramer, M.2
  • 37
    • 0000466705 scopus 로고    scopus 로고
    • Nonlinear network structures for feedback control
    • Lewis F (1999) Nonlinear network structures for feedback control. Asian J Control 1:205-228
    • (1999) Asian J Control , vol.1 , pp. 205-228
    • Lewis, F.1
  • 39
    • 0023331258 scopus 로고
    • An introduction to computing with neural nets
    • Lippmann RP (1987) An introduction to computing with neural nets, IEEE ASSP Mag 4(2):4-22
    • (1987) IEEE ASSP Mag , vol.4 , Issue.2 , pp. 4-22
    • Lippmann, R.P.1
  • 40
    • 0024904699 scopus 로고
    • Multifrequency channel decompositions of images and wavelet models
    • Mallat SG (1987) Multifrequency channel decompositions of images and wavelet models. IEEE Trans on ASSP 37(12):20912109
    • (1987) IEEE Trans on ASSP , vol.37 , Issue.12 , pp. 20912109
    • Mallat, S.G.1
  • 41
    • 0029408591 scopus 로고
    • A fuzzy logic controller for an ABS braking system
    • Mauer GF (1995) A fuzzy logic controller for an ABS braking system. IEEE Trans on Fuzzy Systems 3(4):381-388
    • (1995) IEEE Trans on Fuzzy Systems , vol.3 , Issue.4 , pp. 381-388
    • Mauer, G.F.1
  • 42
    • 0000126249 scopus 로고    scopus 로고
    • Successive approximation methods for the solution of optimal control problems
    • Mitter SK (1996) Successive approximation methods for the solution of optimal control problems. Automatica 3:135-149
    • (1996) Automatica , vol.3 , pp. 135-149
    • Mitter, S.K.1
  • 43
    • 0026153906 scopus 로고    scopus 로고
    • Artificial neural networks in process engineering
    • Morris AJ (1996) Artificial neural networks in process engineering. IEE Proc D 138(3):256-266
    • (1996) IEE Proc D , vol.138 , Issue.3 , pp. 256-266
    • Morris, A.J.1
  • 45
    • 0032584927 scopus 로고    scopus 로고
    • Training wavelet networks for nonlinear dynamic input-output modeling
    • Oussar Y, Rivals I, Personnaz L, Dreyfus G (1998) Training wavelet networks for nonlinear dynamic input-output modeling. Neurocomputing 20:173-188
    • (1998) Neurocomputing , vol.20 , pp. 173-188
    • Oussar, Y.1    Rivals, I.2    Personnaz, L.3    Dreyfus, G.4
  • 47
    • 0142043494 scopus 로고    scopus 로고
    • Generalized modeling principles of a nonlinear system with a dynamic fuzzy network
    • Oysal Y, Becerikli Y, Konar A F (2003) Generalized modeling principles of a nonlinear system with a dynamic fuzzy network. Comput Chem Eng 27(11):1657-1664
    • (2003) Comput Chem Eng , vol.27 , Issue.11 , pp. 1657-1664
    • Oysal, Y.1    Becerikli, Y.2    Konar, A.F.3
  • 50
    • 0000014434 scopus 로고    scopus 로고
    • A generalized learning paradigm exploiting the structure of feedforward neural networks
    • Parisi R, Diclaudid ED, Orlandi G, Rao BD (1996) A generalized learning paradigm exploiting the structure of feedforward neural networks. IEEE Trans Neural Networks 7(6): 1450-1460
    • (1996) IEEE Trans Neural Networks , vol.7 , Issue.6 , pp. 1450-1460
    • Parisi, R.1    Diclaudid, E.D.2    Orlandi, G.3    Rao, B.D.4
  • 51
    • 0000106040 scopus 로고
    • Universal approximation using radial-basis function networks
    • Park J, Sandberg I-W (1991) Universal approximation using radial-basis function networks. Neural Comput 3:246-257
    • (1991) Neural Comput , vol.3 , pp. 246-257
    • Park, J.1    Sandberg, I.-W.2
  • 53
    • 0001202597 scopus 로고
    • Learning state space trajectories in recurrent neural networks
    • Pearlmutter B (1989) Learning state space trajectories in recurrent neural networks. Neural Comput 1:263-269
    • (1989) Neural Comput , vol.1 , pp. 263-269
    • Pearlmutter, B.1
  • 54
    • 2442587578 scopus 로고
    • Dynamic system identification using partially recurrent neural networks
    • Pham DT, Liu X (1990) Dynamic system identification using partially recurrent neural networks. J Syst Eng 2(2):4-27
    • (1990) J Syst Eng , vol.2 , Issue.2 , pp. 4-27
    • Pham, D.T.1    Liu, X.2
  • 57
    • 0019915142 scopus 로고
    • New approaches to the dynamics of nonlinear systems with implications for process and control system design
    • Seabor E (ed)
    • Ray WH (1981) New approaches to the dynamics of nonlinear systems with implications for process and control system design. In: Seabor E (ed) Chemical process control II, pp 245-267
    • (1981) Chemical Process Control II , pp. 245-267
    • Ray, W.H.1
  • 59
    • 0022471098 scopus 로고
    • Learning internal representations by back-propagating errors
    • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by back-propagating errors. Nature 323:533-536
    • (1986) Nature , vol.323 , pp. 533-536
    • De Rumelhart1    Hinton, G.E.2    Williams, R.J.3
  • 60
    • 0026954302 scopus 로고
    • Gaussian networks for direct adaptive control
    • Sanner R, Slotine J-JE (1992) Gaussian networks for direct adaptive control. IEEE Trans Neural Netw 13(6):837-863
    • (1992) IEEE Trans Neural Netw , vol.13 , Issue.6 , pp. 837-863
    • Sanner, R.1    Slotine, J.-J.E.2
  • 62
    • 0003658161 scopus 로고
    • Encoding sequential structure in simple recurrent networks
    • School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
    • Servan-Schreiber D, Cleeremans A, McClellans JL (1988) Encoding sequential structure in simple recurrent networks. Technical report CMU-CS-88-183, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
    • (1988) Technical Report , vol.CMU-CS-88-183
    • Servan-Schreiber, D.1    Cleeremans, A.2    McClellans, J.L.3
  • 63
    • 45449126257 scopus 로고    scopus 로고
    • Structure identification of fuzzy model
    • Sugeno M, Kang GT (1998) Structure identification of fuzzy model. Fuzzy Sets Syst 28:15-33
    • (1998) Fuzzy Sets Syst , vol.28 , pp. 15-33
    • Sugeno, M.1    Kang, G.T.2
  • 64
    • 0021892282 scopus 로고
    • Fuzzy identification of systems and its application to modeling and control
    • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans on SMC 15(1):116-132
    • (1985) IEEE Trans on SMC , vol.15 , Issue.1 , pp. 116-132
    • Takagi, T.1    Sugeno, M.2
  • 66
    • 0026644930 scopus 로고
    • Stability analysis and design of fuzzy control systems
    • Tanaka K, Sugeno M (1992) Stability analysis and design of fuzzy control systems. Fuzzy Sets Syst 45(2): 135-156
    • (1992) Fuzzy Sets Syst , vol.45 , Issue.2 , pp. 135-156
    • Tanaka, K.1    Sugeno, M.2
  • 67
    • 0027608046 scopus 로고
    • Fuzzy stability criterion of a class of nonlinear systems
    • Tanaka K, Sugeno M (1993) Fuzzy stability criterion of a class of nonlinear systems. Inform Sci 71(1-2):3-26
    • (1993) Inform Sci , vol.71 , Issue.1-2 , pp. 3-26
    • Tanaka, K.1    Sugeno, M.2
  • 68
    • 0010267834 scopus 로고
    • Learning in recurrent finite difference networks
    • Touretzky DS, Elman JL, Sejnowski TJ, Hinton GE (eds) San Diego, California. Morgan Kaufmann, San Mateo, California
    • Tsung F-S (1990) Learning in recurrent finite difference networks. In: Touretzky DS, Elman JL, Sejnowski TJ, Hinton GE (eds) Proceedings of the connectionist models summer school. San Diego, California. Morgan Kaufmann, San Mateo, California
    • (1990) Proceedings of the Connectionist Models Summer School
    • Tsung, F.-S.1
  • 70
    • 0026928374 scopus 로고
    • Fuzzy basis function, universal approximation, and orthogonal least squares learning
    • Wang LX, Mendel JM (1992) Fuzzy basis function, universal approximation, and orthogonal least squares learning. IEEE Trans Neural Netw 3:807-814
    • (1992) IEEE Trans Neural Netw , vol.3 , pp. 807-814
    • Wang, L.X.1    Mendel, J.M.2
  • 71
    • 0034253794 scopus 로고    scopus 로고
    • Wavelet-based adaptive robust M-estimator for nonlinear system identification
    • Wang D, Romagnoli JA, Safavi AA (2000) Wavelet-based adaptive robust M-estimator for nonlinear system identification. AIChE J 6(8): 1607-1615
    • (2000) AIChE J , vol.6 , Issue.8 , pp. 1607-1615
    • Wang, D.1    Romagnoli, J.A.2    Safavi, A.A.3
  • 73
    • 0031098254 scopus 로고    scopus 로고
    • Using wavelet network in nonparametric estimation
    • Zhang Q (1997) Using wavelet network in nonparametric estimation. IEEE Trans on Neural Netw 8(2):227-236
    • (1997) IEEE Trans on Neural Netw , vol.8 , Issue.2 , pp. 227-236
    • Zhang, Q.1
  • 75
    • 0029323369 scopus 로고
    • Wavelet neural networks for function learning
    • Zhang J, Walter GG et al (1995) Wavelet neural networks for function learning. IEEE Trans Signal Process 43(6): 1485-1497
    • (1995) IEEE Trans Signal Process , vol.43 , Issue.6 , pp. 1485-1497
    • Zhang, J.1    Walter, G.G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.