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.
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