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Volumn , Issue , 2006, Pages 1851-1857

A new method for designing and reduction of neuro-fuzzy systems

Author keywords

[No Author keywords available]

Indexed keywords

NEURO-FUZZY SYSTEMS; WEIGHTED TRIANGULAR NORMS;

EID: 34250744575     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2006.1681957     Document Type: Conference Paper
Times cited : (16)

References (21)
  • 1
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    • Jan
    • D. Chakraborty, N.R. Pal, A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification, IEEE Transactions on Neural Networks, vol. 15, issue 1, Jan. 2004, pp. 110-123.
    • (2004) IEEE Transactions on Neural Networks , vol.15 , Issue.1 , pp. 110-123
    • Chakraborty, D.1    Pal, N.R.2
  • 4
    • 34250784872 scopus 로고    scopus 로고
    • E. Czogala, J. Łȩski, Fuzzy and Neuro-Fuzzy Intelligent Systems, Physica-Verlag, A Springer-Verlag Company, Heidelberg, New York, 2000.
    • E. Czogala, J. Łȩski, Fuzzy and Neuro-Fuzzy Intelligent Systems, Physica-Verlag, A Springer-Verlag Company, Heidelberg, New York, 2000.
  • 5
    • 44949271432 scopus 로고
    • On fuzzy implication
    • C. Fodor, On fuzzy implication, Fuzzy Sets and Systems, vol. 42, pp. 293-300, 1991.
    • (1991) Fuzzy Sets and Systems , vol.42 , pp. 293-300
    • Fodor, C.1
  • 7
    • 0029273384 scopus 로고
    • Neuro-fuzzy modeling and control
    • March
    • J. S. R. Jang and C. T. Sun, Neuro-fuzzy modeling and control, The Proceedings of the IEEE, vol 83, pp. 378-406, March 1995.
    • (1995) The Proceedings of the IEEE , vol.83 , pp. 378-406
    • Jang, J.S.R.1    Sun, C.T.2
  • 10
    • 1542273540 scopus 로고    scopus 로고
    • Computing Derivatives in Interval Type-2 Fuzzy Logic Systems
    • J. M. Mendel, Computing Derivatives in Interval Type-2 Fuzzy Logic Systems, IEEE Trans, on Fuzzy Systems, vol. 12, no. 1, pp. 84-98, 2004.
    • (2004) IEEE Trans, on Fuzzy Systems , vol.12 , Issue.1 , pp. 84-98
    • Mendel, J.M.1
  • 12
    • 0001623515 scopus 로고    scopus 로고
    • Neuro-fuzzy systems for function approximation
    • D. Nauck and R. Kruse. Neuro-fuzzy systems for function approximation, Fuzzy Sets and Systems, vol. 101, pp. 261-271, 1999.
    • (1999) Fuzzy Sets and Systems , vol.101 , pp. 261-271
    • Nauck, D.1    Kruse, R.2
  • 16
    • 14644433722 scopus 로고    scopus 로고
    • Designing and learning of adjustable quasi triangular norms with applications to neuro fuzzy systems
    • Feb
    • L. Rutkowski and K. Cpałka, Designing and learning of adjustable quasi triangular norms with applications to neuro fuzzy systems, IEEE Trans. on Fuzzy Systems, vol. 13, Feb 2005, pp. 140-151.
    • (2005) IEEE Trans. on Fuzzy Systems , vol.13 , pp. 140-151
    • Rutkowski, L.1    Cpałka, K.2
  • 19
    • 0036902547 scopus 로고    scopus 로고
    • Self-adaptive neuro-fuzzy inference systems for classification applications
    • December
    • J. S. Wang and C. S. G. Lee. Self-adaptive neuro-fuzzy inference systems for classification applications, IEEE Trans. on Fuzzy Systems, vol. 10, pp. 790-802, December 2002.
    • (2002) IEEE Trans. on Fuzzy Systems , vol.10 , pp. 790-802
    • Wang, J.S.1    Lee, C.S.G.2
  • 21
    • 10944239650 scopus 로고    scopus 로고
    • A simple neuro-fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images
    • Dec
    • M.E. Yuksel, E. Besdok, A simple neuro-fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images, IEEE Transactions on Fuzzy Systems, vol. 12, issue 6, Dec. 2004, pp. 854-865.
    • (2004) IEEE Transactions on Fuzzy Systems , vol.12 , Issue.6 , pp. 854-865
    • Yuksel, M.E.1    Besdok, E.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.