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Volumn 86, Issue , 2012, Pages 140-149

An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications

Author keywords

Constructive; Evolutionary algorithm; Neural network; Prediction; Pruning

Indexed keywords

AGE-BASED; CLUSTER-BASED; COMPLEX STRUCTURE; CONSTRUCTIVE; CROSSOVER AND MUTATION; EXPONENTIAL GROWTH; HIDDEN NEURONS; INPUT NODE; MACKEY-GLASS TIME SERIES; NUMERICAL RESULTS; PREDICTION PROBLEM; PRUNING; PRUNING ALGORITHMS; REAL-WORLD APPLICATION; SIMPLE STRUCTURES; TRAFFIC FLOW;

EID: 84862800910     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.01.024     Document Type: Article
Times cited : (63)

References (46)
  • 2
    • 0016495091 scopus 로고
    • Linear prediction: a tutorial review
    • Makhoul J. Linear prediction: a tutorial review. Proc. IEEE 1975, 63:561-580.
    • (1975) Proc. IEEE , vol.63 , pp. 561-580
    • Makhoul, J.1
  • 3
    • 0001829246 scopus 로고
    • Introduction to grey system theory
    • Deng J.L. Introduction to grey system theory. J. Grey Syst. 1989, 1:1-24.
    • (1989) J. Grey Syst. , vol.1 , pp. 1-24
    • Deng, J.L.1
  • 4
    • 0000032342 scopus 로고    scopus 로고
    • How effective are neural networks at forecasting and prediction? A review and evaluation
    • Adya M., Collopy F. How effective are neural networks at forecasting and prediction? A review and evaluation. J. Forecast. 1998, 17:481-495.
    • (1998) J. Forecast. , vol.17 , pp. 481-495
    • Adya, M.1    Collopy, F.2
  • 5
    • 79955518933 scopus 로고    scopus 로고
    • Intelligent forecasting system using grey model combined with neural network
    • Yang S.H., Chen Y.P. Intelligent forecasting system using grey model combined with neural network. Int. J. Fuzzy Syst. 2011, 13:8-15.
    • (2011) Int. J. Fuzzy Syst. , vol.13 , pp. 8-15
    • Yang, S.H.1    Chen, Y.P.2
  • 7
    • 13844298048 scopus 로고    scopus 로고
    • Partially connected feedforward neural networks structured by input types
    • Kang S., Isik C. Partially connected feedforward neural networks structured by input types. IEEE Trans. Neural Networks 2005, 16:175-184.
    • (2005) IEEE Trans. Neural Networks , vol.16 , pp. 175-184
    • Kang, S.1    Isik, C.2
  • 8
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989, 2:359-366.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 9
    • 0026910776 scopus 로고
    • Weather sensitive short-term load forecasting using nonfully connected artificial neural network
    • Chen S.T., Yu D.C., Moghaddamjo A.R. Weather sensitive short-term load forecasting using nonfully connected artificial neural network. IEEE Trans. Power Syst. 1991, 7:1098-1105.
    • (1991) IEEE Trans. Power Syst. , vol.7 , pp. 1098-1105
    • Chen, S.T.1    Yu, D.C.2    Moghaddamjo, A.R.3
  • 10
    • 36149029142 scopus 로고
    • Partially connected models of neural networks
    • Canning A., Gardner E. Partially connected models of neural networks. J. Phys. A 1988, 21:3275-3284.
    • (1988) J. Phys. A , vol.21 , pp. 3275-3284
    • Canning, A.1    Gardner, E.2
  • 11
    • 0031240593 scopus 로고    scopus 로고
    • A survey of partially connected neural networks
    • Elizondo D., Fiesler E. A survey of partially connected neural networks. Int. J. Neural Syst. 1997, 8:535-558.
    • (1997) Int. J. Neural Syst. , vol.8 , pp. 535-558
    • Elizondo, D.1    Fiesler, E.2
  • 12
    • 0031146959 scopus 로고    scopus 로고
    • Constructive algorithms for structure learning in feedforward neural networks for regression problems
    • Kwok T.Y., Yeung D.Y. Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Trans. Neural Networks 1997, 8:630-645.
    • (1997) IEEE Trans. Neural Networks , vol.8 , pp. 630-645
    • Kwok, T.Y.1    Yeung, D.Y.2
  • 13
    • 0027662338 scopus 로고
    • Pruning algorithms-A survey
    • Reed R. Pruning algorithms-A survey. IEEE Trans. Neural Networks 1993, 4:740-747.
    • (1993) IEEE Trans. Neural Networks , vol.4 , pp. 740-747
    • Reed, R.1
  • 14
    • 0035505658 scopus 로고    scopus 로고
    • A new pruning heuristic based on variance analysis of sensitivity information
    • Engelbrecht A.P. A new pruning heuristic based on variance analysis of sensitivity information. IEEE Trans. Neural Networks 2001, 12:1386-1399.
    • (2001) IEEE Trans. Neural Networks , vol.12 , pp. 1386-1399
    • Engelbrecht, A.P.1
  • 15
    • 0026017007 scopus 로고
    • Creating artificial neural networks that generalize
    • Sietsma J., Dow R.J.F. Creating artificial neural networks that generalize. Neural Networks 1991, 4:67-79.
    • (1991) Neural Networks , vol.4 , pp. 67-79
    • Sietsma, J.1    Dow, R.J.F.2
  • 16
    • 0025964567 scopus 로고
    • Back-propagation algorithm which varies the number of hidden units
    • Hirose Y., Yamashita K., Hijiya S. Back-propagation algorithm which varies the number of hidden units. Neural Networks 1991, 4:61-66.
    • (1991) Neural Networks , vol.4 , pp. 61-66
    • Hirose, Y.1    Yamashita, K.2    Hijiya, S.3
  • 17
    • 0042525842 scopus 로고    scopus 로고
    • Neural-network construction and selection in nonlinear modeling
    • Rivals I., Personnaz L. Neural-network construction and selection in nonlinear modeling. IEEE Trans. Neural Networks 2003, 14:804-819.
    • (2003) IEEE Trans. Neural Networks , vol.14 , pp. 804-819
    • Rivals, I.1    Personnaz, L.2
  • 18
    • 67349203854 scopus 로고    scopus 로고
    • A new adaptive merging and growing algorithm for designing artificial neural networks
    • Islam Md.M., Sattar Md.A., Amin Md.F., Yao X., Murase K. A new adaptive merging and growing algorithm for designing artificial neural networks. IEEE Trans. Syst. Man Cybern. Part B 2009, 39:705-722.
    • (2009) IEEE Trans. Syst. Man Cybern. Part B , vol.39 , pp. 705-722
    • Islam, M.1    Sattar, M.2    Amin, M.3    Yao, X.4    Murase, K.5
  • 20
    • 80051786833 scopus 로고    scopus 로고
    • Genetic algorithm pruning of probabilistic neural networks in medical disease estimation
    • Mantzaris D., Anastassopoulos G., Adamopoulos A. Genetic algorithm pruning of probabilistic neural networks in medical disease estimation. Neural Networks 2011, 24:831-835.
    • (2011) Neural Networks , vol.24 , pp. 831-835
    • Mantzaris, D.1    Anastassopoulos, G.2    Adamopoulos, A.3
  • 21
    • 84862809068 scopus 로고    scopus 로고
    • Seasonality and neural networks: a new approach
    • Curry B., Morgan P.H. Seasonality and neural networks: a new approach. Int. J. Metaheurist. 2010, 1:181-197.
    • (2010) Int. J. Metaheurist. , vol.1 , pp. 181-197
    • Curry, B.1    Morgan, P.H.2
  • 22
    • 57749092656 scopus 로고    scopus 로고
    • A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks
    • Huang D.-S., Du J.-X. A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Networks 2008, 19:2099-2115.
    • (2008) IEEE Trans. Neural Networks , vol.19 , pp. 2099-2115
    • Huang, D.-S.1    Du, J.-X.2
  • 23
    • 67349163308 scopus 로고    scopus 로고
    • Neuro-immune approach to solve routing problems
    • Masutti T.A.S., de Castro L.N. Neuro-immune approach to solve routing problems. Neurocomputing 2009, 72:2189-2197.
    • (2009) Neurocomputing , vol.72 , pp. 2189-2197
    • Masutti, T.A.S.1    de Castro, L.N.2
  • 25
    • 52149112310 scopus 로고    scopus 로고
    • Hybrid multiobjective evolutionary design for artificial neural networks
    • Goh C.-K., Teoh E.-J., Tan K.C. Hybrid multiobjective evolutionary design for artificial neural networks. IEEE Trans. Neural Networks 2008, 19:1531-1548.
    • (2008) IEEE Trans. Neural Networks , vol.19 , pp. 1531-1548
    • Goh, C.-K.1    Teoh, E.-J.2    Tan, K.C.3
  • 28
    • 34848872651 scopus 로고    scopus 로고
    • An optimization methodology for neural network weights and architectures
    • Ludermir T.B., Yamazaki A., Zanchettin C. An optimization methodology for neural network weights and architectures. IEEE Trans. Neural Networks 2006, 17:1452-1459.
    • (2006) IEEE Trans. Neural Networks , vol.17 , pp. 1452-1459
    • Ludermir, T.B.1    Yamazaki, A.2    Zanchettin, C.3
  • 29
    • 0003123930 scopus 로고    scopus 로고
    • Forecasting with artificial neural networks: the state of the art
    • Zhang G., Patuwo B.E., Hu M.Y. Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 1998, 14:35-62.
    • (1998) Int. J. Forecast. , vol.14 , pp. 35-62
    • Zhang, G.1    Patuwo, B.E.2    Hu, M.Y.3
  • 30
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart D.E., Hinton G.E., Wiliams R.J. Learning representations by back-propagating errors. Nature 1986, 323:533-536.
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Wiliams, R.J.3
  • 32
    • 0028570720 scopus 로고
    • Sensitivity analysis for minimization of input data dimension for feedforward neural network
    • Zurada J.M., Malinowski A., Cloete I. Sensitivity analysis for minimization of input data dimension for feedforward neural network. ISCAS 1994, 447-450.
    • (1994) ISCAS , pp. 447-450
    • Zurada, J.M.1    Malinowski, A.2    Cloete, I.3
  • 33
    • 0031553665 scopus 로고    scopus 로고
    • Perturbation method for deleting redundant inputs of perceptron networks
    • Zurada J.M., Malinowski A., Usui S. Perturbation method for deleting redundant inputs of perceptron networks. Neurocomputing 1997, 14:177-193.
    • (1997) Neurocomputing , vol.14 , pp. 177-193
    • Zurada, J.M.1    Malinowski, A.2    Usui, S.3
  • 35
    • 84862799504 scopus 로고    scopus 로고
    • Price prediction of share market using artificial neural network (ANN)
    • Khan Z.H., Alin T.S., Hussain Md.A. Price prediction of share market using artificial neural network (ANN). Int. J. Comput. Appl. 2011, 22:42-47.
    • (2011) Int. J. Comput. Appl. , vol.22 , pp. 42-47
    • Khan, Z.H.1    Alin, T.S.2    Hussain, M.3
  • 36
    • 11844291975 scopus 로고    scopus 로고
    • On the role of population size and niche radius in fitness sharing
    • Cioppa A.D., Stefano C.D., Marcelli A. On the role of population size and niche radius in fitness sharing. IEEE Trans. Evol. Comput. 2004, 8:580-592.
    • (2004) IEEE Trans. Evol. Comput. , vol.8 , pp. 580-592
    • Cioppa, A.D.1    Stefano, C.D.2    Marcelli, A.3
  • 37
    • 40649090413 scopus 로고    scopus 로고
    • Time series prediction using evolving radial basis function networks with new encoding scheme
    • Du H., Zhang N. Time series prediction using evolving radial basis function networks with new encoding scheme. Neurocomputing 2008, 71:1388-1400.
    • (2008) Neurocomputing , vol.71 , pp. 1388-1400
    • Du, H.1    Zhang, N.2
  • 38
    • 0027601884 scopus 로고
    • ANFIS: adaptive-network-based fuzzy inference system
    • Jang J.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 1993, 23:665-685.
    • (1993) IEEE Trans. Syst. Man Cybern. , vol.23 , pp. 665-685
    • Jang, J.S.R.1
  • 39
    • 33748424239 scopus 로고    scopus 로고
    • The effect of different basis functions on a radial basis function network for time series prediction: a comparative study
    • Harpham C., Dawson C.W. The effect of different basis functions on a radial basis function network for time series prediction: a comparative study. Neurocomputing 2006, 69:2161-2170.
    • (2006) Neurocomputing , vol.69 , pp. 2161-2170
    • Harpham, C.1    Dawson, C.W.2
  • 41
    • 29444444293 scopus 로고    scopus 로고
    • Time-series prediction using a local linear wavelet neural network
    • Chen Y., Yang B., Dong J. Time-series prediction using a local linear wavelet neural network. Neurocomputing 2006, 69:449-465.
    • (2006) Neurocomputing , vol.69 , pp. 449-465
    • Chen, Y.1    Yang, B.2    Dong, J.3
  • 42
    • 0030283350 scopus 로고    scopus 로고
    • Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction
    • Cho K.B., Wang B.H. Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction. Fuzzy Sets Syst. 1996, 83:325-339.
    • (1996) Fuzzy Sets Syst. , vol.83 , pp. 325-339
    • Cho, K.B.1    Wang, B.H.2
  • 43
    • 0037276988 scopus 로고    scopus 로고
    • Tuning of the structure and parameters of a neural network using an improved genetic algorithm
    • Leung F.H.F., Lam H.K., Ling S.H., Tam P.K.S. Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans. Neural Networks 2003, 14:79-88.
    • (2003) IEEE Trans. Neural Networks , vol.14 , pp. 79-88
    • Leung, F.H.F.1    Lam, H.K.2    Ling, S.H.3    Tam, P.K.S.4
  • 44
    • 77955552432 scopus 로고    scopus 로고
    • Adaptive neural network model for time-series forecasting
    • Wong W.K., Xia M., Chu W.C. Adaptive neural network model for time-series forecasting. Eur. J. Oper. Res. 2010, 207:807-816.
    • (2010) Eur. J. Oper. Res. , vol.207 , pp. 807-816
    • Wong, W.K.1    Xia, M.2    Chu, W.C.3
  • 45
    • 0037243071 scopus 로고    scopus 로고
    • Time series forecasting using a hybrid ARIMA and neural network model
    • Zhang G.P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 2003, 50:159-175.
    • (2003) Neurocomputing , vol.50 , pp. 159-175
    • Zhang, G.P.1
  • 46
    • 38749135531 scopus 로고    scopus 로고
    • Adaptive metrics in the nearest neighbours method
    • Kulesh M., Holschneider M., Kurennaya K. Adaptive metrics in the nearest neighbours method. Phys. D 2008, 237:283-291.
    • (2008) Phys. D , vol.237 , pp. 283-291
    • Kulesh, M.1    Holschneider, M.2    Kurennaya, K.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.