Evolutionary polymorphic neural network in chemical process modeling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Gao:2001:CCE,
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author = "Li Gao and Norman W. Loney",
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title = "Evolutionary polymorphic neural network in chemical
process modeling",
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journal = "Computers \& Chemical Engineering",
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year = "2001",
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volume = "25",
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pages = "1403--1410",
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number = "11-12",
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keywords = "genetic algorithms, genetic programming, Evolutionary
polymorphic neural network (EPNN), Neural network,
Process modeling",
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owner = "wlangdon",
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URL = "http://www.sciencedirect.com/science/article/B6TFT-449TFB0-2/2/b9c50f18933d4b739a9d8a2843b45548",
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ISSN = "0098-1354",
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DOI = "doi:10.1016/S0098-1354(01)00708-6",
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abstract = "Evolutionary polymorphic neural network (EPNN) is a
novel approach to modelling dynamic process systems.
This approach has its basis in artificial neural
networks and evolutionary computing. As demonstrated in
the studied dynamic CSTR system, EPNN produces less
error than a traditional recurrent neural network with
a less number of neurons. Furthermore, EPNN performs
networked symbolic regressions for input-output data,
while it performs multiple step ahead prediction
through adaptable feedback structures formed during
evolution. In addition, the extracted symbolic formulae
from EPNN can be used for further theoretical analysis
and process optimisation.",
- }
Genetic Programming entries for
Li Gao
Norman W Loney
Citations