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

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

Original language	Undefined/Unknown
Pages (from-to)	107-126
Number of pages	20
Journal	Cognition, Technology and Work
Volume	5
Publication status	Published - 2003

Keywords

@article{95453378ef4640b0932095976399545d,

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

keywords = "Peer-lijst tijdschrift",

author = "K Li and S Thompson and PA Wieringa and J Peng",

year = "2003",

language = "Undefined/Unknown",

volume = "5",

pages = "107--126",

journal = "Cognition, Technology and Work",

issn = "1435-5558",

publisher = "Springer",

}

TY - JOUR

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

AU - Li, K

AU - Thompson, S

AU - Wieringa, PA

AU - Peng, J

PY - 2003

Y1 - 2003

KW - Peer-lijst tijdschrift

M3 - Article

SN - 1435-5558

VL - 5

SP - 107

EP - 126

JO - Cognition, Technology and Work

JF - Cognition, Technology and Work

ER -