Generalized Constraint Neural Network Model System Parameter Identification

Abstract:

Article Preview

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 et al., "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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Price:

$35.00

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