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题名:
Generalized constraint neural network model system parameter identification
作者: Zhang Shuang; Jin Gang; Xiao Jing; Li Shu; Qin Yu-Ping; Liu Jin-Hua; An Tao; Zhong WEi-Fan
刊名: Advanced Materials Research
出版日期: 2011
卷号: 143-144, 页码:1207-1212
通讯作者: Zhang Shuang
文章类型: 期刊论文
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
URI标识: http://ir.ioe.ac.cn/handle/181551/4074
Appears in Collections:光电工程总体研究室(一室)_期刊论文

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作者单位: 中国科学院光电技术研究所

Recommended Citation:
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.
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