Generalized Constraint Neural Network Model System Parameter Identification

التفاصيل البيبلوغرافية
العنوان: Generalized Constraint Neural Network Model System Parameter Identification
المؤلفون: Zhang, Shuang, Jin, Gang, Xiao, Jing, Li, Shu, Qin, Yu Ping, Liu, Jin Hua, An, Tao, Zhong, Wei Fan
المصدر: Advanced Materials Research; October 2010, Vol. 143 Issue: 1 p1207-1212, 6p
مستخلص: 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.
قاعدة البيانات: Supplemental Index
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Array ( [Name] => TitleSource [Label] => Source [Group] => Src [Data] => Advanced Materials Research; October 2010, Vol. 143 Issue: 1 p1207-1212, 6p )
Array ( [Name] => Abstract [Label] => Abstract [Group] => Ab [Data] => 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. )
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