Artificial Neural Networks as Subsymbolic Process Descriptors
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @PhdThesis{Minns:thesis,
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author = "Anthony William Minns",
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title = "Artificial Neural Networks as Subsymbolic Process
Descriptors",
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school = "UNESCO-IHE Institute for Water Education, Technische
Universiteit Delft",
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year = "1998",
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address = "The Netherlands",
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keywords = "genetic algorithms, genetic programming,
Rainfall-runoff Modelling, ANN, Cellular automata,
Neural networks (Computer science), Logic circuits,
Hydraulic engineering, artificial intelligence,
artificial neural networks, EAs, LCS, lattice gas
dynamics, Storm, Extreme Events, Cantley Estate in
Doncaster, Silk Stream Hendon Lane, Dollis Brook
Colindeep Lane, Catchment, Brent Reservoir, London
Clay, UK, RORB, IMP",
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publisher = "Balkema, Rotterdam",
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isbn13 = "9054104090",
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URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=24a4bb0e44d938f847cdcdd8667c82820b342bb9",
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URL = "https://ihedelftrepository.contentdm.oclc.org/digital/collection/phd1/id/22089/",
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URL = "https://catalogue.nla.gov.au/catalog/1915787.dc_xml",
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size = "140 pages",
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notes = "Section 6.2 brief description of GP performance in
comparison with ANN.
p65 'extreme compactness of the GP expressions'
UK Flood Studies Report (Natural Environment Research
Council, 1975)",
- }
Genetic Programming entries for
Anthony W Minns
Citations