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Volumn 3, Issue , 2002, Pages 1619-1625

Artificial neural network in estimation of battery state-of-charge (SOC) with nonconventional input variables selected by correlation analysis

Author keywords

Artificial neural network; Battery; Correlation analysis; Input variable selection; State of charge

Indexed keywords

CHARGING (BATTERIES); CORRELATION METHODS; ELECTRIC BATTERIES; ELECTRIC CURRENTS; ELECTRIC POTENTIAL;

EID: 0036931056     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (32)

References (13)
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    • Methods for state-of-charge determination and their application
    • S. Piller, Marion Perrin, A. Jossen. Methods for state-of-charge determination and their application. Journal of Power Sources, 96(2001), pp. 113-120.
    • (2001) Journal of Power Sources , vol.96 , pp. 113-120
    • Piller, S.1    Perrin, M.2    Jossen, A.3
  • 4
    • 0032597551 scopus 로고    scopus 로고
    • Input feature selection for real-time transient stability assessment for artificial neural network (ANN) using ANN sensitivity analysis
    • A.G. Bahbah, A.A. Girgis, Input feature selection for real-time transient stability assessment for artificial neural network (ANN) using ANN sensitivity analysis, IEEE International Conference on Power Industry Computer Applications, pp. 295-300, 1999.
    • (1999) IEEE International Conference on Power Industry Computer Applications , pp. 295-300
    • Bahbah, A.G.1    Girgis, A.A.2
  • 5
    • 0012229617 scopus 로고
    • Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring
    • C.C. Peck, A.P. Dhawan, C.M. Meyer, Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring, IEEE International Conference on Neural Networks, vol.2, pp.1115-1122, 1993.
    • (1993) IEEE International Conference on Neural Networks , vol.2 , pp. 1115-1122
    • Peck, C.C.1    Dhawan, A.P.2    Meyer, C.M.3
  • 7
    • 0032204310 scopus 로고    scopus 로고
    • Input variable selection for ANN-based short-term load forecasting
    • Nov.
    • I. Drezga, S. Rahman. Input variable selection for ANN-based short-term load forecasting. IEEE Transactions on Power System, 13(4), pp. 1238-1244, Nov. 1998.
    • (1998) IEEE Transactions on Power System , vol.13 , Issue.4 , pp. 1238-1244
    • Drezga, I.1    Rahman, S.2
  • 11
    • 0035950897 scopus 로고    scopus 로고
    • Novel input variable selection for ANN short-term load forecasting
    • In Chinese
    • S. Gao, Y.D. Shan, Novel input variable selection for ANN short-term load forecasting. Automation of Electric Power Systems, 2001(22), pp.41-44. In Chinese.
    • (2001) Automation of Electric Power Systems , Issue.22 , pp. 41-44
    • Gao, S.1    Shan, Y.D.2
  • 12
    • 0029222377 scopus 로고
    • Non-linear model identification and statistical significance tests and their application to financial modeling
    • A.N. Burgess, Non-linear model identification and statistical significance tests and their application to financial modeling, Fourth International Conference on Artificial Neural Networks, pp. 312-317, 1995.
    • (1995) Fourth International Conference on Artificial Neural Networks , pp. 312-317
    • Burgess, A.N.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.