A new learning algorithm for space invariant Uncoupled
Cellular Neural Network is introduced. Learning is
formulated as an optimisation problem. Genetic
Programming has been selected for creating new
knowledge because they allow the system to find new
rules both near to good ones and far from them, looking
for unknown good control actions. According to the
lattice Cellular Neural Network architecture, Genetic
Programming will be used in deriving the Cloning
Template. Exploration of any stable domain is possible
by the current approach. Details of the algorithm are
discussed and several application results are shown.
%0 Journal Article
%1 DBLP:journals/ijns/RadwanT04a
%A Radwan, Elsayed
%A Tazaki, Eiichiro
%D 2004
%J International Journal of Neural Systems
%K Cellular Networks, Neural algorithms, cloning genetic programming, template
%N 4
%P 247--256
%R doi:10.1142/S0129065704002030
%T Template learning of cellular neural network using
genetic programming
%V 14
%X A new learning algorithm for space invariant Uncoupled
Cellular Neural Network is introduced. Learning is
formulated as an optimisation problem. Genetic
Programming has been selected for creating new
knowledge because they allow the system to find new
rules both near to good ones and far from them, looking
for unknown good control actions. According to the
lattice Cellular Neural Network architecture, Genetic
Programming will be used in deriving the Cloning
Template. Exploration of any stable domain is possible
by the current approach. Details of the algorithm are
discussed and several application results are shown.
@article{DBLP:journals/ijns/RadwanT04a,
abstract = {A new learning algorithm for space invariant Uncoupled
Cellular Neural Network is introduced. Learning is
formulated as an optimisation problem. Genetic
Programming has been selected for creating new
knowledge because they allow the system to find new
rules both near to good ones and far from them, looking
for unknown good control actions. According to the
lattice Cellular Neural Network architecture, Genetic
Programming will be used in deriving the Cloning
Template. Exploration of any stable domain is possible
by the current approach. Details of the algorithm are
discussed and several application results are shown.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Radwan, Elsayed and Tazaki, Eiichiro},
bibsource = {DBLP, http://dblp.uni-trier.de},
biburl = {https://www.bibsonomy.org/bibtex/2af4afefff4681797838399ad4d895e1f/brazovayeye},
doi = {doi:10.1142/S0129065704002030},
interhash = {60ebe2c169c4e0f7393db7288b9e1507},
intrahash = {af4afefff4681797838399ad4d895e1f},
journal = {International Journal of Neural Systems},
keywords = {Cellular Networks, Neural algorithms, cloning genetic programming, template},
notes = {Department of Control and System Engineering, Toin
University of Yokohama, 1614 Kurogane-cho, Aoba-ku,
Yokohama 225-8502, Japan
PMID: 15372702},
number = 4,
pages = {247--256},
timestamp = {2008-06-19T17:50:01.000+0200},
title = {Template learning of cellular neural network using
genetic programming},
volume = 14,
year = 2004
}