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Volumn 162, Issue 1-2, 2003, Pages 87-95

A novel method for training neural networks for time-series prediction in environmental systems

Author keywords

Backpropagation; Environmental prediction; Neural networks; Simulated annealing; Stream dynamics; Time series prediction

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; SIMULATED ANNEALING; TIME SERIES;

EID: 0037390130     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3800(02)00401-5     Document Type: Article
Times cited : (29)

References (10)
  • 1
    • 0033578403 scopus 로고    scopus 로고
    • Modelling primary production in a coastal embayment affected by upwelling using dynamic ecosystem models and artificial neural networks
    • Barciela, R.M., García, E., Fernández, E., 1999. Modelling primary production in a coastal embayment affected by upwelling using dynamic ecosystem models and artificial neural networks. Ecol. Model. 120, 199-211.
    • (1999) Ecol. Model. , vol.120 , pp. 199-211
    • Barciela, R.M.1    García, E.2    Fernández, E.3
  • 2
    • 0030436566 scopus 로고    scopus 로고
    • Revealing dynamics of ecological systems from natural recordings
    • Boudjema, G., Chau, N.P., 1996. Revealing dynamics of ecological systems from natural recordings. Ecol. Model. 91, 15-23.
    • (1996) Ecol. Model. , vol.91 , pp. 15-23
    • Boudjema, G.1    Chau, N.P.2
  • 3
    • 0034273966 scopus 로고    scopus 로고
    • Using artificial neural networks to forecast chaotic time series
    • De Oliveira, K.A., Vannucci, A., da Silva, E.C., 2000. Using artificial neural networks to forecast chaotic time series. Physica A 284, 393-404.
    • (2000) Physica A , vol.284 , pp. 393-404
    • De Oliveira, K.A.1    Vannucci, A.2    Da Silva, E.C.3
  • 4
    • 0034735692 scopus 로고    scopus 로고
    • Case studies on the use of neural networks in eutrophication modeling
    • Karul, C., Soyupak, S., Çilesiz, A.F., Akbay, N., Germen, E., 2000. Case studies on the use of neural networks in eutrophication modeling. Ecol. Model. 134, 145-152.
    • (2000) Ecol. Model. , vol.134 , pp. 145-152
    • Karul, C.1    Soyupak, S.2    Çilesiz, A.F.3    Akbay, N.4    Germen, E.5
  • 5
    • 0344604541 scopus 로고    scopus 로고
    • Artificial neural networks as a tool in ecological modelling, an introduction
    • Lek, S., Guégan, J.F., 1999. Artificial neural networks as a tool in ecological modelling, an introduction. Ecol. Model. 120, 65-73.
    • (1999) Ecol. Model. , vol.120 , pp. 65-73
    • Lek, S.1    Guégan, J.F.2
  • 6
    • 0037102687 scopus 로고    scopus 로고
    • Illuminating the &black box&: A randomization approach for understanding variable contributions in artificial neural networks
    • Olden, J.D., Jackson, D.A., 2002. Illuminating the &black box&: a randomization approach for understanding variable contributions in artificial neural networks. Ecol. Model. 154, 135-150.
    • (2002) Ecol. Model. , vol.154 , pp. 135-150
    • Olden, J.D.1    Jackson, D.A.2
  • 7
    • 0032090662 scopus 로고    scopus 로고
    • Forecasting S & P 500 stock index futures with a hybrid AI system
    • Tsaih, R., Hsu, Y., Lai, C.C., 1998. Forecasting S & P 500 stock index futures with a hybrid AI system. Decision Support Syst. 23, 161-174.
    • (1998) Decision Support Syst. , vol.23 , pp. 161-174
    • Tsaih, R.1    Hsu, Y.2    Lai, C.C.3
  • 8
    • 0033578379 scopus 로고    scopus 로고
    • Neural network architecture selection: New Bayesian perspectives in predictive modelling. Application to a soil hydrology problem
    • Vila, J.-P., Wagner, V., Neveu, P., Voltz, M., Lagacherie, P., 1999. Neural network architecture selection: new Bayesian perspectives in predictive modelling. Application to a soil hydrology problem. Ecol. Model. 120, 119-130.
    • (1999) Ecol. Model. , vol.120 , pp. 119-130
    • Vila, J.-P.1    Wagner, V.2    Neveu, P.3    Voltz, M.4    Lagacherie, P.5
  • 9
    • 0033019602 scopus 로고    scopus 로고
    • Short term streamflow forecasting using artificial neural networks
    • Zealand, C.M., Burn, D.H., Simonovic, S.P., 1999. Short term streamflow forecasting using artificial neural networks. J. Hydrol. 214, 32-48.
    • (1999) J. Hydrol. , vol.214 , pp. 32-48
    • Zealand, C.M.1    Burn, D.H.2    Simonovic, S.P.3
  • 10
    • 0343052623 scopus 로고    scopus 로고
    • Rainfall estimation using artificial neural network group
    • Zhang, M., Fulcher, J., Scofield, R.A., 1997. Rainfall estimation using artificial neural network group. Neurocomputing 16, 97-115.
    • (1997) Neurocomputing , vol.16 , pp. 97-115
    • Zhang, M.1    Fulcher, J.2    Scofield, R.A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.