메뉴 건너뛰기




Volumn 364-365, Issue , 2016, Pages 197-212

Randomized algorithms for nonlinear system identification with deep learning modification

Author keywords

Deep learning; Nonlinear system modeling; Randomized algorithms

Indexed keywords

ALGORITHMS; LEARNING SYSTEMS; NONLINEAR SYSTEMS; RELIGIOUS BUILDINGS;

EID: 84983314971     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2015.09.048     Document Type: Article
Times cited : (80)

References (27)
  • 1
    • 0001578518 scopus 로고
    • A learning algorithm for Boltzmann machines
    • [1] Ackley, D.H., Hinton, G.E., Sejnowski, T.J., A learning algorithm for Boltzmann machines. Cogn. Sci. 9 (1985), 147–169.
    • (1985) Cogn. Sci. , vol.9 , pp. 147-169
    • Ackley, D.H.1    Hinton, G.E.2    Sejnowski, T.J.3
  • 2
    • 84894439375 scopus 로고    scopus 로고
    • Fast decorrelated neural network ensembles with random weights
    • [2] Alhamdoosh, M., Wang, D., Fast decorrelated neural network ensembles with random weights. Inf. Sci. 264 (2014), 104–117.
    • (2014) Inf. Sci. , vol.264 , pp. 104-117
    • Alhamdoosh, M.1    Wang, D.2
  • 3
    • 67651049775 scopus 로고    scopus 로고
    • Justifying and generalizing contrastive divergence
    • [3] Bengio, Y., Delalleau, O., Justifying and generalizing contrastive divergence. Neural Comput. 21:6 (2009), 1601–1621.
    • (2009) Neural Comput. , vol.21 , Issue.6 , pp. 1601-1621
    • Bengio, Y.1    Delalleau, O.2
  • 4
    • 84857855190 scopus 로고    scopus 로고
    • Random search for hyper-parameter optimization
    • [4] Bergstra, J., Bengio, Y., Random search for hyper-parameter optimization. J. Mach. Learn. Res., 2011, 281–305.
    • (2011) J. Mach. Learn. Res. , pp. 281-305
    • Bergstra, J.1    Bengio, Y.2
  • 6
    • 84875891731 scopus 로고    scopus 로고
    • Noise-shaping gradient descent-based online adaptation algorithms for digital calibration of analog circuits
    • [6] Chakrabartty, S., Shaga, R.K., Aono, K., Noise-shaping gradient descent-based online adaptation algorithms for digital calibration of analog circuits. IEEE Trans. Neural Netw. Learn. Syst. 24:4 (2013), 554–565.
    • (2013) IEEE Trans. Neural Netw. Learn. Syst. , vol.24 , Issue.4 , pp. 554-565
    • Chakrabartty, S.1    Shaga, R.K.2    Aono, K.3
  • 11
    • 84866791477 scopus 로고    scopus 로고
    • System identification: a Wiener–Hammerstein benchmark
    • [11] Hjalmarsson, H., Rojas, C.R., Rivera, D.E., System identification: a Wiener–Hammerstein benchmark. Control Eng. Pract. 20 (2012), 1095–1096.
    • (2012) Control Eng. Pract. , vol.20 , pp. 1095-1096
    • Hjalmarsson, H.1    Rojas, C.R.2    Rivera, D.E.3
  • 12
    • 0029403793 scopus 로고
    • Stochastic choice of basis functions in adaptive function approximation and the functional-link net
    • [12] Igelnik, B., Pao, Y.-H., Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans. Neural Netw. 6:2 (1995), 1320–1329.
    • (1995) IEEE Trans. Neural Netw. , vol.6 , Issue.2 , pp. 1320-1329
    • Igelnik, B.1    Pao, Y.-H.2
  • 13
    • 0030410676 scopus 로고    scopus 로고
    • Identification of nonlinear dynamical systems using multilayered neural networks
    • [13] Jagannathan, S., Lewis, F.L., Identification of nonlinear dynamical systems using multilayered neural networks. Automatica 32:12 (1996), 1707–1712.
    • (1996) Automatica , vol.32 , Issue.12 , pp. 1707-1712
    • Jagannathan, S.1    Lewis, F.L.2
  • 14
    • 0027601884 scopus 로고
    • ANFIS: Adaptive-network-based fuzzy inference system
    • [14] Jang, J.S., ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23 (1993), 665–685.
    • (1993) IEEE Trans. Syst. Man Cybern. , vol.23 , pp. 665-685
    • Jang, J.S.1
  • 15
    • 0037276988 scopus 로고    scopus 로고
    • Tuning of the structure and parameters of a neural network using an improved genetic algorithm
    • [15] Leung, F.H.F., Lam, H.K., Ling, S.H., Tam, P.K.S., Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans. Neural Netw. 14 (2003), 79–88.
    • (2003) IEEE Trans. Neural Netw. , vol.14 , pp. 79-88
    • Leung, F.H.F.1    Lam, H.K.2    Ling, S.H.3    Tam, P.K.S.4
  • 17
    • 0000169232 scopus 로고
    • An algorithm for least-squares estimation of nonlinear parameters
    • [17] Marquardt, D., An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11:2 (1963), 431–441.
    • (1963) SIAM J. Appl. Math. , vol.11 , Issue.2 , pp. 431-441
    • Marquardt, D.1
  • 18
    • 0034187785 scopus 로고    scopus 로고
    • Neuro-fuzzy rule generation: survey in soft computing framework
    • [18] Mitra, S., Hayashi, Y., Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans. Neural Netw. 11:3 (2000), 748–769.
    • (2000) IEEE Trans. Neural Netw. , vol.11 , Issue.3 , pp. 748-769
    • Mitra, S.1    Hayashi, Y.2
  • 19
    • 0026117466 scopus 로고
    • Gradient methods for optimization of dynamical systems containing neural networks
    • [19] Narendra, K.S., Parthasarathy, K., Gradient methods for optimization of dynamical systems containing neural networks. IEEE Trans. Neural Netw. 3:2 (1991), 252–262.
    • (1991) IEEE Trans. Neural Netw. , vol.3 , Issue.2 , pp. 252-262
    • Narendra, K.S.1    Parthasarathy, K.2
  • 20
    • 0042525842 scopus 로고    scopus 로고
    • Neural-network construction and selection in nonlinear modeling
    • [20] Rivals, I., Personnaz, L., Neural-network construction and selection in nonlinear modeling. IEEE Trans. Neural Netw. 14:4 (2003), 804–820.
    • (2003) IEEE Trans. Neural Netw. , vol.14 , Issue.4 , pp. 804-820
    • Rivals, I.1    Personnaz, L.2
  • 23
    • 83655163812 scopus 로고    scopus 로고
    • Robust initialization of a Jordan network with recurrent constrained learning
    • [23] Song, Q., Robust initialization of a Jordan network with recurrent constrained learning. IEEE Trans. Neural Netw. 22:12 (2011), 2460–2473.
    • (2011) IEEE Trans. Neural Netw. , vol.22 , Issue.12 , pp. 2460-2473
    • Song, Q.1
  • 24
    • 0027544110 scopus 로고
    • A fuzzy logic based approach to qualitative modeling
    • [24] Sugeno, M., Yasukawa, T., A fuzzy logic based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1:1 (1993), 7–31.
    • (1993) IEEE Trans. Fuzzy Syst. , vol.1 , Issue.1 , pp. 7-31
    • Sugeno, M.1    Yasukawa, T.2
  • 25
    • 84880787590 scopus 로고    scopus 로고
    • Learning the pseudoinverse solution to network weights
    • [25] J. Tapson, A. van Schaik, Learning the pseudoinverse solution to network weights, Neural Netw. 45 (2013) 94–100.
    • (2013) Neural Netw. , vol.45 , pp. 94-100
    • Tapson, J.1    van Schaik, A.2
  • 26
    • 0030087291 scopus 로고    scopus 로고
    • Complex systems modeling via fuzzy logic
    • [26] Wang, L., Langari, R., Complex systems modeling via fuzzy logic. IEEE Trans. Syst. Man Cybern. 26:1 (1996), 100–106.
    • (1996) IEEE Trans. Syst. Man Cybern. , vol.26 , Issue.1 , pp. 100-106
    • Wang, L.1    Langari, R.2
  • 27
    • 77958067568 scopus 로고    scopus 로고
    • Automated nonlinear system modeling with multiple fuzzy neural networks and kernel smoothing
    • [27] Yu, W., Li, X., Automated nonlinear system modeling with multiple fuzzy neural networks and kernel smoothing. Int. J. Neural Syst. 20:5 (2010), 429–435.
    • (2010) Int. J. Neural Syst. , vol.20 , Issue.5 , pp. 429-435
    • Yu, W.1    Li, X.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.