메뉴 건너뛰기




Volumn 7, Issue 19, 2007, Pages 2812-2817

Feedforward neural network for solving partial differential equations

Author keywords

Burger's equation; Feedforward neural network; Partial differential equation

Indexed keywords

BOUNDARY CONDITIONS; FEEDFORWARD NEURAL NETWORKS; ONE DIMENSIONAL;

EID: 36049028481     PISSN: 18125654     EISSN: 18125662     Source Type: Journal    
DOI: 10.3923/jas.2007.2812.2817     Document Type: Article
Times cited : (39)

References (15)
  • 3
    • 0035926652 scopus 로고    scopus 로고
    • Solving N-body Problems with Neural Networks
    • Cruito, M., C. Monterola and C. Saloma, 2001. Solving N-body Problems with Neural Networks. Phys. Rev. Lett., pp: 4741-4744.
    • (2001) Phys. Rev. Lett , pp. 4741-4744
    • Cruito, M.1    Monterola, C.2    Saloma, C.3
  • 4
    • 0028543366 scopus 로고
    • Training feedforward Neural Network with the Marquardt algorithm
    • Hagan, M.T. and M.B. Menhaj, 1994. Training feedforward Neural Network with the Marquardt algorithm. IEEE Trans. Neural Networks, 5: 989-993.
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 989-993
    • Hagan, M.T.1    Menhaj, M.B.2
  • 8
    • 2342444094 scopus 로고    scopus 로고
    • Numerical solution of the Burger's equation by the least-squares quadratic B-spline finite element method
    • Kutluay, S., A. Esen and I. Dag, 2004. Numerical solution of the Burger's equation by the least-squares quadratic B-spline finite element method. J. Comput. Applied Math., 167: 21-33.
    • (2004) J. Comput. Applied Math , vol.167 , pp. 21-33
    • Kutluay, S.1    Esen, A.2    Dag, I.3
  • 10
    • 0001518167 scopus 로고
    • On the Convergence of the LMS algorithm with adaptive learning rate for linear feedforward neural networks
    • Luo, Z., 1991. On the Convergence of the LMS algorithm with adaptive learning rate for linear feedforward neural networks. Neural Comput., 3: 213-225.
    • (1991) Neural Comput , vol.3 , pp. 213-225
    • Luo, Z.1
  • 11
    • 0032002859 scopus 로고    scopus 로고
    • Charactering the dynamic of constrained physical systems with unsupervised neural network
    • Monterda, C. and C. Saloma, 1998. Charactering the dynamic of constrained physical systems with unsupervised neural network. Phys. Rev., E57: 1247-1250.
    • (1998) Phys. Rev , vol.E57 , pp. 1247-1250
    • Monterda, C.1    Saloma, C.2
  • 13
    • 0344715043 scopus 로고
    • The numerical solution of the neural network training problems
    • 1089, Center for Supercomputing Research and Development, University of Illinois, Urbana
    • Saarineen, S., R. Bramley and G. Cybenko, 1991. The numerical solution of the neural network training problems. CRSD Report 1089, Center for Supercomputing Research and Development, University of Illinois, Urbana.
    • (1991) CRSD Report
    • Saarineen, S.1    Bramley, R.2    Cybenko, G.3
  • 14
    • 36048940554 scopus 로고    scopus 로고
    • An FPGA implementation of neural optimization of block truncation coding for image/video compression. Microprocessor Microsystems
    • In Press
    • Saif, S., H.M. Abbas, S.M. Nassar and A.A. Wahdan, 2006. An FPGA implementation of neural optimization of block truncation coding for image/video compression. Microprocessor Microsystems, (In Press).
    • (2006)
    • Saif, S.1    Abbas, H.M.2    Nassar, S.M.3    Wahdan, A.A.4
  • 15
    • 0033990683 scopus 로고    scopus 로고
    • A weight initialization method for improving training speed in feedforward neural network
    • Yam, J. and T. Chow, 2000. A weight initialization method for improving training speed in feedforward neural network. Neurocomputing, 30: 219-232.
    • (2000) Neurocomputing , vol.30 , pp. 219-232
    • Yam, J.1    Chow, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.