메뉴 건너뛰기




Volumn 54, Issue 11-12, 2007, Pages 1353-1366

Support vector fuzzy adaptive network in regression analysis

Author keywords

Fuzzy adaptive network; Fuzzy logic; Neural network; Statistical learning theory; Support vector machines

Indexed keywords

FUZZY LOGIC; NEURAL NETWORKS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

EID: 35748961989     PISSN: 08981221     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.camwa.2007.03.006     Document Type: Article
Times cited : (9)

References (27)
  • 3
  • 7
    • 84964556084 scopus 로고    scopus 로고
    • J.T. Jeng, C.C. Chuang, Initial structure selection for neural networks and fuzzy neural networks based on support vector regression with outliers, in: Proceedings of the 9th International Conference on Neural Information Processing, vol. 2, 2002, pp. 909-914
  • 8
    • 3142685488 scopus 로고    scopus 로고
    • Selection of initial structures with support vector regression for fuzzy neural networks
    • Jeng J.T., and Chuang C.C. Selection of initial structures with support vector regression for fuzzy neural networks. International Journal of Fuzzy Systems 6 2 (2004) 63-70
    • (2004) International Journal of Fuzzy Systems , vol.6 , Issue.2 , pp. 63-70
    • Jeng, J.T.1    Chuang, C.C.2
  • 10
    • 35748945549 scopus 로고    scopus 로고
    • W.C. Chan, C.W. Chan, K.C. Cheung, C.J. Harris, Modeling of nonlinear dynamic systems using support vector neural networks, in: Preprints IFAC Symposium AIRTC 2000, Budapest, Hungary, 2000, pp. 217-222
  • 12
    • 0035792598 scopus 로고    scopus 로고
    • W.C. Chan, C.W. Chan, K.C. Cheung, C.J. Harris, Support vector recurrent neurofuzzy networks in modeling nonlinear systems with correlated noise, in: Joint 9th IFSA World Congress and 20th NAFIPS International Conference, vol. 1, 2001, pp. 545-550
  • 13
    • 2442561025 scopus 로고    scopus 로고
    • Improving the generalization ability of neuro-fuzzy systems by ε-insensitive learning
    • Leski J.M. Improving the generalization ability of neuro-fuzzy systems by ε-insensitive learning. International Journal Applied Mathematics and Computer Science 12 3 (2002) 437-447
    • (2002) International Journal Applied Mathematics and Computer Science , vol.12 , Issue.3 , pp. 437-447
    • Leski, J.M.1
  • 15
    • 1642365701 scopus 로고    scopus 로고
    • A new approach to fuzzy modeling using an extended kernel method
    • Kim J., Kim T., and Suga Y. A new approach to fuzzy modeling using an extended kernel method. IEICE Transactions on Fundamentals E86-A 9 (2003)
    • (2003) IEICE Transactions on Fundamentals , vol.E86-A , Issue.9
    • Kim, J.1    Kim, T.2    Suga, Y.3
  • 17
    • 0038170239 scopus 로고    scopus 로고
    • K.Y. Choy, C.W. Chan, Modeling of river discharges using neural networks derived from support vector regression, in: FUZZ'03, The 12th IEEE International Conference on Fuzzy Systems, vol. 2, 2003, pp. 1321-1326
  • 18
    • 0042591514 scopus 로고    scopus 로고
    • Support vector fuzzy regression machines
    • Hong D.H., and Hwang C. Support vector fuzzy regression machines. Fuzzy Sets and Systems 138 (2003) 271-281
    • (2003) Fuzzy Sets and Systems , vol.138 , pp. 271-281
    • Hong, D.H.1    Hwang, C.2
  • 19
    • 0021892282 scopus 로고
    • Fuzzy identification of systems and its application to modeling and control
    • Takagi T., and Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 15 1 (1985) 116-132
    • (1985) IEEE Transactions on Systems, Man, and Cybernetics , vol.15 , Issue.1 , pp. 116-132
    • Takagi, T.1    Sugeno, M.2
  • 20
    • 38249035267 scopus 로고
    • Fuzzy data analysis by possibilistic linear models
    • Tanaka H. Fuzzy data analysis by possibilistic linear models. Fuzzy Sets and Systems 24 (1987) 363-375
    • (1987) Fuzzy Sets and Systems , vol.24 , pp. 363-375
    • Tanaka, H.1
  • 21
    • 0027266182 scopus 로고
    • Functional equivalence between radial basis function network and fuzzy inference systems
    • Jang J.S.R., and Sun C.T. Functional equivalence between radial basis function network and fuzzy inference systems. IEEE Transactions on Neural Networks 4 1 (1993) 156-159
    • (1993) IEEE Transactions on Neural Networks , vol.4 , Issue.1 , pp. 156-159
    • Jang, J.S.R.1    Sun, C.T.2
  • 22
    • 35748930999 scopus 로고    scopus 로고
    • J. Shen, Fusing of support vector machines and soft computing for pattern recognition and regression, Ph.D. Dissertation, Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, 2005
  • 23
    • 34249753618 scopus 로고
    • Support vector network
    • Cortes C., and Vapnik V. Support vector network. Machine Learning 20 (1995) 273-297
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 27
    • 35748957286 scopus 로고    scopus 로고
    • Y. Jiao, Fuzzy adaptive network and applications to humanistic systems, Ph.D. Dissertation, Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, Kansas, 2003


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.