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Volumn , Issue , 2010, Pages 1728-1732

System identification under non-negativity constraints

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMIC SYSTEM MODELING; GENERAL METHOD; GRADIENT DESCENT; KULLBACK LEIBLER DIVERGENCE; NON-NEGATIVITY; NONSTATIONARY SIGNAL PROCESSING; PHYSICAL CHARACTERISTICS; WEIGHT UPDATE;

EID: 84863796766     PISSN: 22195491     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (5)

References (9)
  • 1
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    • Two point step size gradient methods
    • J. Barzilai and J. Borwein. Two point step size gradient methods. IMA J. Numer. Anal, 8(1):141-148, 1988.
    • (1988) IMA J. Numer. Anal , vol.8 , Issue.1 , pp. 141-148
    • Barzilai, J.1    Borwein, J.2
  • 2
    • 74849106173 scopus 로고    scopus 로고
    • Nonnegative least-squares image deblurring: Improved gradient projection approaches
    • F. Benvenuto, R. Zanella, L. Zanni, and M. Bertero. Nonnegative least-squares image deblurring: improved gradient projection approaches. Inverse Problems, 26:025004, 2010.
    • (2010) Inverse Problems , vol.26 , pp. 025004
    • Benvenuto, F.1    Zanella, R.2    Zanni, L.3    Bertero, M.4
  • 3
    • 0023416451 scopus 로고
    • Projected gradient methods for linearly constrained problems
    • P. Calamai and J. Moré. Projected gradient methods for linearly constrained problems. Mathematical Programming, 39(1):93-116, 1987.
    • (1987) Mathematical Programming , vol.39 , Issue.1 , pp. 93-116
    • Calamai, P.1    Moré, J.2
  • 5
    • 0035336716 scopus 로고    scopus 로고
    • A general method to devise maximum-likelihood signal restoration multiplicative algorithms with non-negativity constraints
    • H. Lanteri, M. Roche, O. Cuevas, and C. Aime. A general method to devise maximum-likelihood signal restoration multiplicative algorithms with non-negativity constraints. Signal Processing, 81(5):945-974, 2001.
    • (2001) Signal Processing , vol.81 , Issue.5 , pp. 945-974
    • Lanteri, H.1    Roche, M.2    Cuevas, O.3    Aime, C.4
  • 6
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • D. Lee and H. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788-791, 1999.
    • (1999) Nature , vol.401 , Issue.6755 , pp. 788-791
    • Lee, D.1    Seung, H.2
  • 8
    • 35548969471 scopus 로고    scopus 로고
    • Projected gradient methods for nonnegative matrix factorization
    • C. Lin. Projected gradient methods for nonnegative matrix factorization. Neural Computation, 19(10):2756- 2779, 2007.
    • (2007) Neural Computation , vol.19 , Issue.10 , pp. 2756-2779
    • Lin, C.1
  • 9
    • 0011007625 scopus 로고
    • Iterative maximum likelihood estimator and Bayesian algorithms for image reconstruction in astronomy
    • Baltimore
    • J. Llacer and J. Nunez. Iterative maximum likelihood estimator and Bayesian algorithms for image reconstruction in astronomy. The Restoration of HST Images and Spectra, Baltimore, 1990.
    • (1990) The Restoration of HST Images and Spectra
    • Llacer, J.1    Nunez, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.