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Volumn 9, Issue 3, 1998, Pages 473-482

An analytical framework for local feedforward networks

Author keywords

Feedforward neural networks; Gradient methods; Interference; Learning systems; Multilayer perceptron; Spatially local learning; Spatially local networks

Indexed keywords

BACKPROPAGATION; LEARNING ALGORITHMS; LEARNING SYSTEMS;

EID: 0032074269     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.668889     Document Type: Article
Times cited : (21)

References (13)
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  • 2
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    • An introduction to connectionist learning control systems
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    • Baker, W.1    Farrell, J.2
  • 5
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    • Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic interference
    • R. French, "Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic interference," in Proc. 16th Annu. Cognitive Sci. Soc. Conf., 1994, vol. 5, pp. 207-220.
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  • 6
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    • Connectionist learning for control
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    • A. G. Barto, "Connectionist learning for control," in Neural Networks for Control, I. W. T. Miller, R. S. Sutton, and P. J. Werbos, Eds. Cambridge, MA: MIT Press, 1990, pp. 5-58.
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    • Barto, A.G.1
  • 8
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    • Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
    • M. A. Cohen and S. Grossberg, "Absolute stability of global pattern formation and parallel memory storage by competitive neural networks," IEEE Trans. Syst., Man, Cybern., vol. 5, pp. 815-826, 1983.
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  • 9
    • 0021776661 scopus 로고
    • A massively parallel architecture for a self-organizing neural pattern recognition machine
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  • 10
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  • 12
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    • Multilayer feedforward networks are universal approximators
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.