메뉴 건너뛰기




Volumn 142, Issue 2, 2004, Pages 221-242

Extended neuro-fuzzy models of multilayer perceptrons

Author keywords

Artificial neural network; f duality; Functional equality; Fuzzy rule based system; Interactive or; Neuro fuzzy modeling; Universal approximation

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL METHODS; COMPUTER SIMULATION; EXPERT SYSTEMS; LEARNING SYSTEMS; LINGUISTICS; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS; MULTILAYER NEURAL NETWORKS;

EID: 0742303092     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(03)00244-6     Document Type: Article
Times cited : (24)

References (71)
  • 1
    • 0005619704 scopus 로고    scopus 로고
    • Generalization of adaptive neuro-fuzzy inference systems
    • Azeem M.F., et al. Generalization of adaptive neuro-fuzzy inference systems. IEEE Trans. Neural Networks. 11:2000;1332-1346.
    • (2000) IEEE Trans. Neural Networks , vol.11 , pp. 1332-1346
    • Azeem, M.F.1
  • 2
    • 0026820187 scopus 로고
    • Fast learning process of multilayer neural networks using recursive least squares technique
    • Azimi-Sadjadi M.R., Liou R. Fast learning process of multilayer neural networks using recursive least squares technique. IEEE Trans. Signal Process. 40(2):1992;446-450.
    • (1992) IEEE Trans. Signal Process. , vol.40 , Issue.2 , pp. 446-450
    • Azimi-Sadjadi, M.R.1    Liou, R.2
  • 3
    • 0031233348 scopus 로고    scopus 로고
    • Are artificial neural networks black boxes
    • Benitez J.M., et al. Are artificial neural networks black boxes. IEEE Trans. Neural Networks. 8:1997;1156-1164.
    • (1997) IEEE Trans. Neural Networks , vol.8 , pp. 1156-1164
    • Benitez, J.M.1
  • 5
    • 0001269716 scopus 로고
    • On the equivalence of neural nets and fuzzy expert systems
    • Buckley J.J. On the equivalence of neural nets and fuzzy expert systems. Fuzzy Sets and Systems. 53:1993;129-134.
    • (1993) Fuzzy Sets and Systems , vol.53 , pp. 129-134
    • Buckley, J.J.1
  • 6
    • 0034187390 scopus 로고    scopus 로고
    • SEPARATE: A machine learning method based on semi-global partitions
    • Castro J.L. SEPARATE. a machine learning method based on semi-global partitions IEEE Trans. Neural Networks. 11:2000;710-720.
    • (2000) IEEE Trans. Neural Networks , vol.11 , pp. 710-720
    • Castro, J.L.1
  • 7
    • 0025449162 scopus 로고
    • Parallel recursive prediction error algorithms for training layered neural network
    • Chen S., et al. Parallel recursive prediction error algorithms for training layered neural network. Internat. J. Control. 51:1990;1215-1228.
    • (1990) Internat. J. Control , vol.51 , pp. 1215-1228
    • Chen, S.1
  • 8
    • 0026366218 scopus 로고
    • Neural-network-based fuzzy logic control and decision system
    • Chin-Teng Lin, C.S. George Lee, Neural-network-based fuzzy logic control and decision system, IEEE Trans. Comput. 40 (1991) 1320-1336.
    • (1991) IEEE Trans. Comput. , vol.40 , pp. 1320-1336
    • Lin, C.-T.1    Lee, C.S.G.2
  • 9
    • 0031268660 scopus 로고    scopus 로고
    • An accelerated recurrent network training algorithm using IIR filter model and recursive least squares method
    • Chow T.W.S., Cho S.Y. An accelerated recurrent network training algorithm using IIR filter model and recursive least squares method. IEEE Trans. Circuits Systems - I: Fund. Theory Appl. 44:1997;1082-1086.
    • (1997) IEEE Trans. Circuits Systems - I: Fund. Theory Appl. , vol.44 , pp. 1082-1086
    • Chow, T.W.S.1    Cho, S.Y.2
  • 10
    • 0026837224 scopus 로고
    • A machine learning method for generation of a neural network architecture: A continuous ID3 algorithm
    • K.J. Cios, Ning Liu, A machine learning method for generation of a neural network architecture: a continuous ID3 algorithm, IEEE Trans. Neural Networks 3 (1992) 280-291.
    • (1992) IEEE Trans. Neural Networks , vol.3 , pp. 280-291
    • Cios, K.J.1    Liu, N.2
  • 11
    • 0024861871 scopus 로고
    • Approximations by superposition of a sigmoidal function
    • Cybenko G. Approximations by superposition of a sigmoidal function. Math. Control, Signals Systems. 2:1989;303-314.
    • (1989) Math. Control, Signals Systems , vol.2 , pp. 303-314
    • Cybenko, G.1
  • 12
    • 0020163947 scopus 로고
    • Basic concepts for a theory of evaluation: The aggregative operator
    • Dombi J. Basic concepts for a theory of evaluation. the aggregative operator Europ. J. Oper. Res. 10:1982;282-293.
    • (1982) Europ. J. Oper. Res. , vol.10 , pp. 282-293
    • Dombi, J.1
  • 14
    • 0003000735 scopus 로고
    • Faster-learning variations on back-propagation: An empirical study
    • S.E. Fahlman, Faster-learning variations on back-propagation: an empirical study, in: Proc. of 1988 Connectionist Models Summer School, 1988, pp. 38-51.
    • (1988) Proc. of 1988 Connectionist Models Summer School , pp. 38-51
    • Fahlman, S.E.1
  • 15
    • 0026136149 scopus 로고
    • Learning algorithms of layered neural networks via extended Kalman filters
    • Fukuda W.K.T., Tzafestas S.G. Learning algorithms of layered neural networks via extended Kalman filters. Internat. J. Systems Sci. 22:1991;753-768.
    • (1991) Internat. J. Systems Sci. , vol.22 , pp. 753-768
    • Fukuda, W.K.T.1    Tzafestas, S.G.2
  • 16
    • 0034187078 scopus 로고    scopus 로고
    • General fuzzy min-max neural networks for clustering and classification
    • Gabrays B., Bargiela A. General fuzzy min-max neural networks for clustering and classification. IEEE Trans. Neural Networks. 11:2000;769-783.
    • (2000) IEEE Trans. Neural Networks , vol.11 , pp. 769-783
    • Gabrays, B.1    Bargiela, A.2
  • 17
    • 0021518209 scopus 로고
    • Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
    • Geman S., Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6:1984;721-741.
    • (1984) IEEE Trans. Pattern Anal. Mach. Intell. , vol.6 , pp. 721-741
    • Geman, S.1    Geman, D.2
  • 19
    • 0032980978 scopus 로고    scopus 로고
    • Subset-based training and pruning of sigmoid neural networks
    • Guian Zhou, J. Si, Subset-based training and pruning of sigmoid neural networks, Neural Networks 12 (1999) 79-89.
    • (1999) Neural Networks , vol.12 , pp. 79-89
    • Zhou, G.1    Si, J.2
  • 20
    • 0028543366 scopus 로고
    • Training feedforward networks with the Marquardt algorithm
    • Hagan M.T., Menhaj M.B. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks. 5:1994;989-993.
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 989-993
    • Hagan, M.T.1    Menhaj, M.B.2
  • 22
    • 0033732353 scopus 로고    scopus 로고
    • The equivalence between fuzzy logic systems and feedforward neural networks
    • Hong-Xing Li, C.L. Philip Chen, The equivalence between fuzzy logic systems and feedforward neural networks, IEEE Trans. Neural Networks, 11 (2000) 356-365.
    • (2000) IEEE Trans. Neural Networks , vol.11 , pp. 356-365
    • Li, H.-X.1    Chen, C.L.P.2
  • 23
    • 0025627940 scopus 로고
    • Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
    • Hornik K., Stinchcombe M., White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks. 3:1990;551-560.
    • (1990) Neural Networks , vol.3 , pp. 551-560
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 24
    • 0030142632 scopus 로고    scopus 로고
    • Extending the functional equivalence of radial basis function networks and fuzzy inference systems
    • Hunt K.J., et al. Extending the functional equivalence of radial basis function networks and fuzzy inference systems. IEEE Trans. Neural Networks. 7:1996;776-781.
    • (1996) IEEE Trans. Neural Networks , vol.7 , pp. 776-781
    • Hunt, K.J.1
  • 25
    • 0024137490 scopus 로고
    • Increased rate of convergence through learning rate adaptation
    • Jacobs R.A. Increased rate of convergence through learning rate adaptation. Neural Networks. 1:1988;295-307.
    • (1988) Neural Networks , vol.1 , pp. 295-307
    • Jacobs, R.A.1
  • 26
    • 0027601884 scopus 로고
    • ANFIS: Adaptive-network-based fuzzy inference system
    • Jang J.-S. ANFIS. adaptive-network-based fuzzy inference system IEEE Trans. Systems, Man, Cybernet. 23:1993;665-685.
    • (1993) IEEE Trans. Systems, Man, Cybernet. , vol.23 , pp. 665-685
    • Jang, J.-S.1
  • 27
    • 0028739689 scopus 로고
    • Structure determination in fuzzy modeling: A fuzzy CART approach
    • Orlando, FL
    • J.-S. Jang, Structure determination in fuzzy modeling: a fuzzy CART approach, in: Proc. of the 3rd IEEE Conf. on Fuzzy Systems, vol. 1, Orlando, FL, 1994, pp. 480-485.
    • (1994) Proc. of the 3rd IEEE Conf. on Fuzzy Systems , vol.1 , pp. 480-485
    • Jang, J.-S.1
  • 28
    • 0027266182 scopus 로고
    • Functional equivalence between radial basis function networks and fuzzy inference systems
    • Jang J.-S., Sun C.-T. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans. Neural Networks. 4:1993;156-159.
    • (1993) IEEE Trans. Neural Networks , vol.4 , pp. 156-159
    • Jang, J.-S.1    Sun, C.-T.2
  • 29
    • 0029273384 scopus 로고
    • Neuro-fuzzy modeling and control
    • J.-S. Jang, C.-T. Sun, Neuro-fuzzy modeling and control, in: Proc. IEEE 83(3) (1995) 378-406.
    • (1995) Proc. IEEE , vol.83 , Issue.3 , pp. 378-406
    • Jang, J.-S.1    Sun, C.-T.2
  • 30
    • 0034482649 scopus 로고    scopus 로고
    • Fuzzy perceptron neural networks for classifiers with numerical data and linguistic rules as inputs
    • Jia-Lin Chen, Jyh-Yeong Chang, Fuzzy perceptron neural networks for classifiers with numerical data and linguistic rules as inputs, IEEE Trans. Fuzzy System 8 (2000) 730-745.
    • (2000) IEEE Trans. Fuzzy System , vol.8 , pp. 730-745
    • Chen, J.-L.1    Chang, J.-Y.2
  • 31
    • 0030104509 scopus 로고    scopus 로고
    • Accelerating the training of feedforward neural networks using generalized Hebbian rules for initializing the internal representations
    • Karayiannis N.B. Accelerating the training of feedforward neural networks using generalized Hebbian rules for initializing the internal representations. IEEE Trans. Neural Networks. 7:1996;419-426.
    • (1996) IEEE Trans. Neural Networks , vol.7 , pp. 419-426
    • Karayiannis, N.B.1
  • 32
    • 0032845493 scopus 로고    scopus 로고
    • HyFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems
    • Kim J., Kasabov N. HyFIS. adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems Neural Networks. 12:1999;1301-1319.
    • (1999) Neural Networks , vol.12 , pp. 1301-1319
    • Kim, J.1    Kasabov, N.2
  • 33
    • 0024715766 scopus 로고
    • An adaptive least squares algorithm for the efficient training of artificial neural networks
    • Kollias S., Anastassiou D. An adaptive least squares algorithm for the efficient training of artificial neural networks. IEEE Trans. Circuits systems. 36:1989;1092-1101.
    • (1989) IEEE Trans. Circuits Systems , vol.36 , pp. 1092-1101
    • Kollias, S.1    Anastassiou, D.2
  • 34
    • 0026841022 scopus 로고
    • A real-time learning algorithm for a multilayered neural network based on the extended Kalman filter
    • Liguni Y., et al. A real-time learning algorithm for a multilayered neural network based on the extended Kalman filter. IEEE Trans. Signal Process. 40:1992;959-966.
    • (1992) IEEE Trans. Signal Process. , vol.40 , pp. 959-966
    • Liguni, Y.1
  • 35
    • 0028336556 scopus 로고
    • Genetic evolution of the topology and weight distribution of neural networks
    • Maniezzo V. Genetic evolution of the topology and weight distribution of neural networks. IEEE Trans. Neural Networks. 5:1994;39-53.
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 39-53
    • Maniezzo, V.1
  • 36
    • 0026053207 scopus 로고
    • Fractally configured neural networks
    • Merrill J.W.L., Port R.F. Fractally configured neural networks. Neural Networks. 4:1991;53-60.
    • (1991) Neural Networks , vol.4 , pp. 53-60
    • Merrill, J.W.L.1    Port, R.F.2
  • 38
    • 0025399567 scopus 로고
    • Identification and control of dynamical systems using neural networks
    • Narendra K.S., Parthsarathy K. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks. 1:1990;4-27.
    • (1990) IEEE Trans. Neural Networks , vol.1 , pp. 4-27
    • Narendra, K.S.1    Parthsarathy, K.2
  • 39
    • 0033716744 scopus 로고    scopus 로고
    • Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm
    • Ngia L.S.H., Sjoberg J. Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm. IEEE Trans. Signal Process. 48:2000;1915-1927.
    • (2000) IEEE Trans. Signal Process. , vol.48 , pp. 1915-1927
    • Ngia, L.S.H.1    Sjoberg, J.2
  • 40
    • 0025536870 scopus 로고
    • Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
    • San Diego, CA
    • D. Nguyen, B. Widrow, Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights, in: Int. Joint Conf. Neural Networks, vol. 3, San Diego, CA, 1990, pp. 21-26.
    • (1990) Int. Joint Conf. Neural Networks , vol.3 , pp. 21-26
    • Nguyen, D.1    Widrow, B.2
  • 42
    • 0023602770 scopus 로고
    • Optimal algorithms for adaptive networks: Second order back propagation, second order direct propagation, and second order Hebbian learning
    • IEEE Press, Piscataway, NJ
    • D.B. Parker, Optimal algorithms for adaptive networks: second order back propagation, second order direct propagation, and second order Hebbian learning, in: Proc. IEEE Conf. on Neural Networks, vol. II, IEEE Press, Piscataway, NJ, 1987, pp. 593-600.
    • (1987) Proc. IEEE Conf. on Neural Networks , vol.2 , pp. 593-600
    • Parker, D.B.1
  • 43
    • 0001406440 scopus 로고
    • A mean field theory learning algorithm for neural networks
    • IEEE Press, NJ
    • C. Peterson, J.R. Anderson, A mean field theory learning algorithm for neural networks, Complex Systems, vol. 1, IEEE Press, NJ, 1987, pp. 995-1019.
    • (1987) Complex Systems , vol.1 , pp. 995-1019
    • Peterson, C.1    Anderson, J.R.2
  • 44
    • 0001857179 scopus 로고
    • Learning efficient classification procedures and their application to chess end-games
    • R.S. et al. Michalski. Los Altos, CA: Morgan Kaufmann
    • Quinlan I.R. Learning efficient classification procedures and their application to chess end-games. Michalski R.S., et al. Machine Learning. 1983;Morgan Kaufmann, Los Altos, CA.
    • (1983) Machine Learning
    • Quinlan, I.R.1
  • 45
    • 0025841422 scopus 로고
    • Rescaling of variables in back propagation learning
    • Rigler A.K., Irvine J.M., Vogl T.P. Rescaling of variables in back propagation learning. Neural Networks. 4:1991;225-229.
    • (1991) Neural Networks , vol.4 , pp. 225-229
    • Rigler, A.K.1    Irvine, J.M.2    Vogl, T.P.3
  • 46
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart D.E., Hinton G.E., Williams R.J. Learning representations by back-propagating errors. Nature. 32:1986;533-536.
    • (1986) Nature , vol.32 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 47
    • 0026923239 scopus 로고
    • Optimal filtering algorithms for fast learning in feedforward neural networks
    • Shah S., et al. Optimal filtering algorithms for fast learning in feedforward neural networks. Neural Networks. 5:1992;779-787.
    • (1992) Neural Networks , vol.5 , pp. 779-787
    • Shah, S.1
  • 48
    • 0029296388 scopus 로고
    • A fuzzy neural network for rule acquiring on fuzzy control system
    • Shann J.J., Fu H.C. A fuzzy neural network for rule acquiring on fuzzy control system. Fuzzy Sets and Systems. 71:1995;345-357.
    • (1995) Fuzzy Sets and Systems , vol.71 , pp. 345-357
    • Shann, J.J.1    Fu, H.C.2
  • 49
    • 0032584923 scopus 로고    scopus 로고
    • Local cluster neural net: Architecture, training and applications
    • Shlomo Geva, Kurt Malmstorm, Joaquin Sitte, Local cluster neural net: architecture, training and applications, Neurocomputing 20 (1998) 35-56.
    • (1998) Neurocomputing , vol.20 , pp. 35-56
    • Geva, S.1    Malmstorm, K.2    Sitte, J.3
  • 50
    • 0026923902 scopus 로고
    • On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
    • Shin-ichi Horikawa, et al., On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm, IEEE Trans. Neural Networks 3 (1992) 801-806.
    • (1992) IEEE Trans. Neural Networks , vol.3 , pp. 801-806
    • Horikawa, S.-I.1
  • 51
    • 0018530680 scopus 로고
    • Symmetric summation: A class of operations on fuzzy sets
    • Silvert W. Symmetric summation: a class of operations on fuzzy sets. IEEE Trans. Systems Man Cybernet. 9:1979;657-659.
    • (1979) IEEE Trans. Systems Man Cybernet. , vol.9 , pp. 657-659
    • Silvert, W.1
  • 52
    • 0027542561 scopus 로고
    • Fuzzy min-max neural networks-Part 2: Clustering
    • Simpson P.K. Fuzzy min-max neural networks-Part 2. clustering IEEE Trans. Fuzzy Systems. 1:1993;32-45.
    • (1993) IEEE Trans. Fuzzy Systems , vol.1 , pp. 32-45
    • Simpson, P.K.1
  • 53
    • 0026927202 scopus 로고
    • Fuzzy min-max neural networks-Part 1: Classification
    • Simpson P.K. Fuzzy min-max neural networks-Part 1. classification IEEE Trans. Neural Networks. 3:1994;776-786.
    • (1994) IEEE Trans. Neural Networks , vol.3 , pp. 776-786
    • Simpson, P.K.1
  • 54
    • 0032935466 scopus 로고    scopus 로고
    • Training multilayer neural networks using fast global learning algorithm - Least-squares and penalized optimization methods
    • Siu-yeung Cho, T.W.S. Chow, Training multilayer neural networks using fast global learning algorithm - least-squares and penalized optimization methods, Neurocomputing 25 (1999) 115-131.
    • (1999) Neurocomputing , vol.25 , pp. 115-131
    • Cho, S.-Y.1    Chow, T.W.S.2
  • 55
    • 0026837985 scopus 로고
    • Designing multilayer perceptrons from nearest-neighbor systems
    • Smyth S.G. Designing multilayer perceptrons from nearest-neighbor systems. IEEE Trans. Neural Networks. 3:1992;329-333.
    • (1992) IEEE Trans. Neural Networks , vol.3 , pp. 329-333
    • Smyth, S.G.1
  • 56
    • 45449126257 scopus 로고
    • Structure identification of fuzzy model
    • Sugeno M., Kang G.T. Structure identification of fuzzy model. Fuzzy Sets and Systems. 28:1988;15-33.
    • (1988) Fuzzy Sets and Systems , vol.28 , pp. 15-33
    • Sugeno, M.1    Kang, G.T.2
  • 57
    • 0028370964 scopus 로고
    • Rule-base structure identification in an adaptive-network-based fuzzy inference system
    • Sun C.-T. Rule-base structure identification in an adaptive-network-based fuzzy inference system. IEEE Trans. Fuzzy Systems. 2:1994;64-73.
    • (1994) IEEE Trans. Fuzzy Systems , vol.2 , pp. 64-73
    • Sun, C.-T.1
  • 58
    • 0021892282 scopus 로고
    • Fuzzy identification of systems and its applications to modeling and control
    • Takagi T., Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Systems, Man, and Cybernet. 15:1985;116-132.
    • (1985) IEEE Trans. Systems, Man, and Cybernet. , vol.15 , pp. 116-132
    • Takagi, T.1    Sugeno, M.2
  • 59
    • 0026711368 scopus 로고
    • Improving the convergence of the back-propagation algorithm
    • Van Ooyen A., Nienhuis B. Improving the convergence of the back-propagation algorithm. Neural Networks. 5:1992;465-471.
    • (1992) Neural Networks , vol.5 , pp. 465-471
    • Van Ooyen, A.1    Nienhuis, B.2
  • 60
    • 0026928374 scopus 로고
    • Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
    • Wang L.-X. Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. Neural Networks. 3:1992;807-814.
    • (1992) IEEE Trans. Neural Networks , vol.3 , pp. 807-814
    • Wang, L.-X.1
  • 61
    • 0023541050 scopus 로고
    • Learning algorithms for connectionist networks: Applied gradient methods of nonlinear optimization
    • IEEE Press, Piscataway, NJ
    • R.L. Watrous, Learning algorithms for connectionist networks: applied gradient methods of nonlinear optimization, in: Proc. IEEE Conf. on Neural Networks, vol. II, IEEE Press, Piscataway, NJ, 1987, pp. 619-627.
    • (1987) Proc. IEEE Conf. on Neural Networks , vol.2 , pp. 619-627
    • Watrous, R.L.1
  • 62
    • 0025724253 scopus 로고
    • A method for self-determination of adaptive learning rates in back propagation
    • Weir M.K. A method for self-determination of adaptive learning rates in back propagation. Neural Networks. 4:1991;371-379.
    • (1991) Neural Networks , vol.4 , pp. 371-379
    • Weir, M.K.1
  • 63
    • 0026955395 scopus 로고
    • Avoiding false local minima by proper initialization of connections
    • Wessels L.F.A., Barnard E. Avoiding false local minima by proper initialization of connections. IEEE Trans. Neural Networks. 3:1992;899-905.
    • (1992) IEEE Trans. Neural Networks , vol.3 , pp. 899-905
    • Wessels, L.F.A.1    Barnard, E.2
  • 64
    • 0028498541 scopus 로고
    • On the initialization and optimization of multilayer perceptrons
    • Weymaere N., Martens J.-P. On the initialization and optimization of multilayer perceptrons. IEEE Trans. Neural Networks. 5:1994;738-751.
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 738-751
    • Weymaere, N.1    Martens, J.-P.2
  • 65
    • 0025477595 scopus 로고
    • Genetic algorithms and neural networks: Optimizing connections and connectivity
    • Whitley D., et al. Genetic algorithms and neural networks. optimizing connections and connectivity Computing. 14:1990;347-361.
    • (1990) Computing , vol.14 , pp. 347-361
    • Whitley, D.1
  • 66
    • 0025488663 scopus 로고
    • 30 Years of adaptive neural networks: Perceptron, madline, and backpropagation
    • B. Widrow, M.A. Lehr, 30 years of adaptive neural networks: perceptron, madline, and backpropagation, in: Proc. IEEE 78 (1990) 1415-1442.
    • (1990) Proc. IEEE , vol.78 , pp. 1415-1442
    • Widrow, B.1    Lehr, M.A.2
  • 67
    • 44949280565 scopus 로고
    • Connectives and quantifiers in fuzzy logic
    • Yager R.R. Connectives and quantifiers in fuzzy logic. Fuzzy Sets and Systems. 40:1991;39-75.
    • (1991) Fuzzy Sets and Systems , vol.40 , pp. 39-75
    • Yager, R.R.1
  • 68
    • 0035272407 scopus 로고    scopus 로고
    • Feedforward networks training speed enhancement by optimal initialization of the synaptic coefficients
    • Yam J.Y.F., Chow T.W.S. Feedforward networks training speed enhancement by optimal initialization of the synaptic coefficients. IEEE Trans. Neural Networks. 12:2001;430-434.
    • (2001) IEEE Trans. Neural Networks , vol.12 , pp. 430-434
    • Yam, J.Y.F.1    Chow, T.W.S.2
  • 69
    • 0027668649 scopus 로고
    • Extended backpropagation algorithm
    • Yam Y.F., Chow T.W.S. Extended backpropagation algorithm. Electron. Lett. 29:1993;1701-1702.
    • (1993) Electron. Lett. , vol.29 , pp. 1701-1702
    • Yam, Y.F.1    Chow, T.W.S.2
  • 70
    • 0002834406 scopus 로고    scopus 로고
    • A self-organizing neural-network-based fuzzy system
    • Yin Wang, Gang Rong, A self-organizing neural-network-based fuzzy system, Fuzzy Sets and Systems 103 (1999) 1-11.
    • (1999) Fuzzy Sets and Systems , vol.103 , pp. 1-11
    • Wang, Y.1    Rong, G.2
  • 71
    • 0032144731 scopus 로고    scopus 로고
    • A modified back-propagation method to avoid false local minima
    • Yutaka Fukuoka, et al., A modified back-propagation method to avoid false local minima, Neural Networks 11 (1998) 1059-1072.
    • (1998) Neural Networks , vol.11 , pp. 1059-1072
    • Fukuoka, Y.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.