메뉴 건너뛰기




Volumn 326, Issue , 2015, Pages 381-392

A hybrid Gravitational Search Algorithm and back-propagation for training feedforward neural networks

Author keywords

Back Propagation algorithm; Feedforward neural networks; Gravitational search algorithm

Indexed keywords

BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; BENCHMARKING; FEEDFORWARD NEURAL NETWORKS; GRAVITATION; HEURISTIC ALGORITHMS; LEARNING ALGORITHMS; NEURAL NETWORKS; OPTIMIZATION; SYSTEMS ENGINEERING;

EID: 84910659460     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-11680-8_30     Document Type: Conference Paper
Times cited : (11)

References (27)
  • 1
    • 53849126165 scopus 로고    scopus 로고
    • Neural networks and statistical techniques: A review of applications
    • Paliwal, M., Kumar, U.A.: Neural networks and statistical techniques: A review of applications. Expert Systems with Applications 36(1), 2–17 (2009)
    • (2009) Expert Systems with Applications , vol.36 , Issue.1 , pp. 2-17
    • Paliwal, M.1    Kumar, U.A.2
  • 3
    • 0024866495 scopus 로고
    • On the approximate realization of continuous mappings by neural networks
    • Funahashi, K.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2(3), 183–192 (1989)
    • (1989) Neural Networks , vol.2 , Issue.3 , pp. 183-192
    • Funahashi, K.1
  • 5
    • 77957899139 scopus 로고    scopus 로고
    • Clustered-hybrid multilayer perceptron network for pattern recognition application
    • Mat Isa, N.: Clustered-hybrid multilayer perceptron network for pattern recognition application. Applied Soft Computing 11(1), 1457–1466 (2011)
    • (2011) Applied Soft Computing , vol.11 , Issue.1 , pp. 1457-1466
    • Mat Isa, N.1
  • 6
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Homik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1989)
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Homik, K.1    Stinchcombe, M.2    White, H.3
  • 7
    • 0001934378 scopus 로고    scopus 로고
    • Approximating polynomial functions by feedforward artificial neural network: Capacity analysis and design
    • Malakooti, B., Zhou, Y.: Approximating polynomial functions by feedforward artificial neural network: capacity analysis and design. Applied Mathematics and Computation 90(1), 27–52 (1998)
    • (1998) Applied Mathematics and Computation , vol.90 , Issue.1 , pp. 27-52
    • Malakooti, B.1    Zhou, Y.2
  • 9
    • 0028543366 scopus 로고
    • Training feedforward networks with the Marquardt algorithm
    • Hagar, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Transactions Neural Networks 5(6), 989–993 (1994)
    • (1994) IEEE Transactions Neural Networks , vol.5 , Issue.6 , pp. 989-993
    • Hagar, M.T.1    Menhaj, M.B.2
  • 10
    • 0002603370 scopus 로고
    • An adaptive conjugate gradient learning algorithm for efficient training of neural networks
    • Adeli, H., Hung, S.L.: An adaptive conjugate gradient learning algorithm for efficient training of neural networks. Applied Mathematics and Computation 62(1), 81–102 (1994)
    • (1994) Applied Mathematics and Computation , vol.62 , Issue.1 , pp. 81-102
    • Adeli, H.1    Hung, S.L.2
  • 11
    • 67349220042 scopus 로고    scopus 로고
    • An online gradient method with momentum for two-layer feedforward neural networks
    • Zhang, N.: An online gradient method with momentum for two-layer feedforward neural networks. Applied Mathematics and Computation 212(2), 488–498 (2009)
    • (2009) Applied Mathematics and Computation , vol.212 , Issue.2 , pp. 488-498
    • Zhang, N.1
  • 12
    • 0000195251 scopus 로고    scopus 로고
    • Comparing backpropagation with a genetic algorithm for neural network training
    • Gupta, J.N.D., Sexton, R.S.: Comparing backpropagation with a genetic algorithm for neural network training. Omega 27(6), 679–684 (1999)
    • (1999) Omega , vol.27 , Issue.6 , pp. 679-684
    • Gupta, J.N.D.1    Sexton, R.S.2
  • 13
    • 84862858496 scopus 로고    scopus 로고
    • Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm
    • Mirjalili, S.A., Mohd Hashim, S.Z., Sardroudi, H.M.: Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Applied Mathematics and Computation 218(22), 11125–11137 (2012)
    • (2012) Applied Mathematics and Computation , vol.218 , Issue.22 , pp. 11125-11137
    • Mirjalili, S.A.1    Mohd Hashim, S.Z.2    Sardroudi, H.M.3
  • 15
    • 33847379879 scopus 로고    scopus 로고
    • A hybrid particle swarm optimization back-propagation algorithm for feedforward neural network training
    • Zhang, J.R., Zhang, J., Lock, T.M., Lyu, M.R.: A hybrid particle swarm optimization back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation 185(2), 1026–1037 (2007)
    • (2007) Applied Mathematics and Computation , vol.185 , Issue.2 , pp. 1026-1037
    • Zhang, J.R.1    Zhang, J.2    Lock, T.M.3    Lyu, M.R.4
  • 18
    • 0030082551 scopus 로고    scopus 로고
    • Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems
    • Dorigo, M., Maniezzo, V., Golomi, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics 26(1), 29–41 (1996)
    • (1996) Man, and Cybernetics , vol.26 , Issue.1 , pp. 29-41
    • Dorigo, M.1    Maniezzo, V.2    Golomi, A.3
  • 19
    • 71749096569 scopus 로고    scopus 로고
    • Evolutionary artificial neural networks by multi-dimensional particle swarm optimization
    • Kiranyaz, S., Ince, T., Yildirim, A., Gabbouj, M.: Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Networks 22(10), 1448–1462 (2009)
    • (2009) Neural Networks , vol.22 , Issue.10 , pp. 1448-1462
    • Kiranyaz, S.1    Ince, T.2    Yildirim, A.3    Gabbouj, M.4
  • 21
    • 84946906636 scopus 로고    scopus 로고
    • Why’GSA: A gravitational search algorithm’ is not genuinely based on the law of gravity
    • Gauci, M., Dodd, T.J., Grob, R.: Why’GSA: a gravitational search algorithm’ is not genuinely based on the law of gravity. Natural Computing 11(4), 719–720 (2012)
    • (2012) Natural Computing , vol.11 , Issue.4 , pp. 719-720
    • Gauci, M.1    Dodd, T.J.2    Grob, R.3
  • 24
    • 0036437286 scopus 로고    scopus 로고
    • Simple Explanation of the No-Free-Lunch Theorem and Its Implications
    • Ho, Y.C., Pepyne, D.L.: Simple Explanation of the No-Free-Lunch Theorem and Its Implications. Journal of Optimization Theory and Applications 115(3), 549–570 (2002)
    • (2002) Journal of Optimization Theory and Applications , vol.115 , Issue.3 , pp. 549-570
    • Ho, Y.C.1    Pepyne, D.L.2
  • 26
    • 84885640902 scopus 로고    scopus 로고
    • A new optimization algorithm for single hidden layer feedforward neural networks
    • Li, L.K., Shao, S., Yiu, K.F.C.: A new optimization algorithm for single hidden layer feedforward neural networks. Applied Soft Computing 13(5), 2857–2862 (2013)
    • (2013) Applied Soft Computing , vol.13 , Issue.5 , pp. 2857-2862
    • Li, L.K.1    Shao, S.2    Yiu, K.F.C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.