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Volumn 108, Issue 1, 1999, Pages 49-58

Fuzzy number neural networks

Author keywords

Fuzzy numbers; Learning; Neural networks

Indexed keywords

CONSTRAINT THEORY; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; MULTILAYER NEURAL NETWORKS; NUMBER THEORY; OPTIMIZATION;

EID: 0033361980     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(97)00339-4     Document Type: Article
Times cited : (26)

References (10)
  • 7
    • 2542540236 scopus 로고
    • Learning of fuzzy neural networks from fuzzy inputs and fuzzy targets
    • Seoul, South Korea
    • H. Ishibuchi, K. Kwon, H. Tanaka, Learning of fuzzy neural networks from fuzzy inputs and fuzzy targets, Proc. 5th IFSA World Congr., Seoul, South Korea, vol. I, 1993, pp. 147-150.
    • (1993) Proc. 5th IFSA World Congr. , vol.1 , pp. 147-150
    • Ishibuchi, H.1    Kwon, K.2    Tanaka, H.3
  • 8
    • 0029770199 scopus 로고    scopus 로고
    • Fuzzy regression analysis by neural networks with non-symmetric fuzzy number weights
    • H. Ishibuchi, M. Nii, Fuzzy regression analysis by neural networks with non-symmetric fuzzy number weights, Proc. Internat. Conf. on Neural Networks, vol. II, 1996, pp. 1191-1196.
    • (1996) Proc. Internat. Conf. on Neural Networks , vol.2 , pp. 1191-1196
    • Ishibuchi, H.1    Nii, M.2
  • 10
    • 0025503558 scopus 로고
    • Backpropagation through time: What it does and how to do it
    • P.J. Werbos, Backpropagation through time: what it does and how to do it, Proc. IEEE 78 (10) (1990).
    • (1990) Proc. IEEE , vol.78 , Issue.10
    • Werbos, P.J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.