Paper Title:
Generalized Constraint Neural Network Model System Parameter Identification
  Abstract

By analyzing and deducing generalized constraint neural network (GCNN) with model some present theories, the identification method of the m-input n-output (MINO) and multiple-input multiple–output (MIMO) systems is acquired. It is possible to improve the transparency of the black box through the practical test. This identification method is useful to enhance identification of GCNN model’s parameters, moreover, the identification ability of the neural network black box system model is further made better.

  Info
Periodical
Advanced Materials Research (Volumes 143-144)
Edited by
H. Wang, B.J. Zhang, X.Z. Liu, D.Z. Luo, S.B. Zhong
Pages
1207-1212
DOI
10.4028/www.scientific.net/AMR.143-144.1207
Citation
S. Zhang, G. Jin, J. Xiao, S. Li, Y. P. Qin, J. H. Liu, T. An, W. F. Zhong, "Generalized Constraint Neural Network Model System Parameter Identification", Advanced Materials Research, Vols. 143-144, pp. 1207-1212, 2011
Online since
October 2010
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$32.00
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