An Improved Normalized Mutual Information Variable Selection Algorithm for Neural Network-Based Soft Sensors

In this paper, normalized mutual information feature selection (NMIFS) and tabu search (TS) are integrated to develop a new variable selection algorithm for soft sensors. NMIFS is applied to select influential variables contributing to the output variable and avoids selecting redundant variables by...

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Detalles Bibliográficos
Autores principales: Sun, Kai, Tian, Pengxin, Qi, Huanning, Ma, Fengying, Yang, Genke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960561/
https://www.ncbi.nlm.nih.gov/pubmed/31817459
http://dx.doi.org/10.3390/s19245368

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960561/
https://www.ncbi.nlm.nih.gov/pubmed/31817459
http://dx.doi.org/10.3390/s19245368

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