Neural networks and genetic algorithms can support human supervisory control to reduce fossil fuel power plant emissions

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	107-126
Number of pages	20
Journal	Cognition, Technology and Work
Volume	5(2)
DOIs	https://doi.org/10.1007/s10111-002-0107-6
Publication status	Published - Jun 2003

Access to Document

@article{29969ada6a4041909b3467660e92413f,

title = "Neural networks and genetic algorithms can support human supervisory control to reduce fossil fuel power plant emissions",

author = "Kang Li and Stephen Thompson and P.A. Wieringa and Peng, \{Jian Xun\} and G.R. Duan",

year = "2003",

month = jun,

doi = "10.1007/s10111-002-0107-6",

language = "English",

volume = "5(2)",

pages = "107--126",

journal = "Cognition, Technology and Work",

issn = "1435-5558",

publisher = "Springer London",

}

TY - JOUR

T1 - Neural networks and genetic algorithms can support human supervisory control to reduce fossil fuel power plant emissions

AU - Li, Kang

AU - Thompson, Stephen

AU - Wieringa, P.A.

AU - Peng, Jian Xun

AU - Duan, G.R.

PY - 2003/6

Y1 - 2003/6

U2 - 10.1007/s10111-002-0107-6

DO - 10.1007/s10111-002-0107-6

M3 - Article

SN - 1435-5558

VL - 5(2)

SP - 107

EP - 126

JO - Cognition, Technology and Work

JF - Cognition, Technology and Work

ER -