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	Generalized constraint neural network model system parameter identification
	Zhang Shuang; Jin Gang; Xiao Jing; Li Shu; Qin Yu-Ping; Liu Jin-Hua; An Tao; Zhong WEi-Fan
刊名	Advanced Materials Research
	2011
卷号	143-144页码:1207-1212
通讯作者	Zhang Shuang
中文摘要	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.
英文摘要	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.
语种	英语
内容类型	期刊论文
源URL	[http://ir.ioe.ac.cn/handle/181551/4074]
专题	光电技术研究所_光电工程总体研究室（一室）
作者单位	中国科学院光电技术研究所
推荐引用方式 GB/T 7714	Zhang Shuang,Jin Gang,Xiao Jing,et al. Generalized constraint neural network model system parameter identification[J]. Advanced Materials Research,2011,143-144:1207-1212.
APA	Zhang Shuang.,Jin Gang.,Xiao Jing.,Li Shu.,Qin Yu-Ping.,...&Zhong WEi-Fan.(2011).Generalized constraint neural network model system parameter identification.Advanced Materials Research,143-144,1207-1212.
MLA	Zhang Shuang,et al."Generalized constraint neural network model system parameter identification".Advanced Materials Research 143-144(2011):1207-1212.

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