Robust boosting neural networks with random weights for multivariate calibration of complex samples.

Bian, Xihui; Diwu, Pengyao; Zhang, Caixia; Lin, Ligang; Chen, Guohui; Tan, Xiaoyao; Guo, Yugao; Cheng, Bowen

Bian, Xihui; Diwu, Pengyao; Zhang, Caixia; Lin, Ligang; Chen, Guohui; Tan, Xiaoyao; Guo, Yugao; Cheng, Bowen.

Afiliação

Bian X; State Key Laboratory of Separation Membranes and Membrane Processes, Tianjin Polytechnic University, Tianjin, 300387, PR China; School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin, 300387, PR China. Electronic address: bianxihui@mail.nankai.edu.cn.
Diwu P; State Key Laboratory of Separation Membranes and Membrane Processes, Tianjin Polytechnic University, Tianjin, 300387, PR China; School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin, 300387, PR China.
Zhang C; State Key Laboratory of Separation Membranes and Membrane Processes, Tianjin Polytechnic University, Tianjin, 300387, PR China; School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin, 300387, PR China.
Lin L; State Key Laboratory of Separation Membranes and Membrane Processes, Tianjin Polytechnic University, Tianjin, 300387, PR China.
Chen G; School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin, 300387, PR China.
Tan X; State Key Laboratory of Separation Membranes and Membrane Processes, Tianjin Polytechnic University, Tianjin, 300387, PR China; School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin, 300387, PR China.
Guo Y; School of Environmental and Chemical Engineering, Tianjin Polytechnic University, Tianjin, 300387, PR China.
Cheng B; State Key Laboratory of Separation Membranes and Membrane Processes, Tianjin Polytechnic University, Tianjin, 300387, PR China.

Anal Chim Acta ; 1009: 20-26, 2018 Jun 07.

Article em En | MEDLINE | ID: mdl-29422128

Palavras-chave

Boosting; Complex samples; Ensemble modeling; Extreme learning machine; Neural networks with random weights; Outlier

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Prognostic_studies Idioma: En Revista: Anal Chim Acta Ano de publicação: 2018 Tipo de documento: Article País de publicação: Holanda

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