DocumentCode
957729
Title
Genetic algorithm for CNN template learning
Author
Kozek, Tibor ; Roska, Tamás ; Chua, Leon O.
Author_Institution
Dept. of Electr. Eng., California Univ., Berkeley, CA, USA
Volume
40
Issue
6
fYear
1993
fDate
6/1/1993 12:00:00 AM
Firstpage
392
Lastpage
402
Abstract
A learning algorithm for space invariant cellular neural networks (CNNs) is described. Learning is formulated as an optimization problem. Exploration of any specified domain of stable CNNs is possible by the current approach. Templates are derived using a genetic optimization algorithm. Details of the algorithm are discussed and several application results are shown. Using this algorithm, propagation-type and gray-scale-output CNNs can also be designed
Keywords
genetic algorithms; learning (artificial intelligence); neural nets; CNN template learning; genetic optimization algorithm; gray-scale-output CNNs; learning algorithm; optimization problem; propagation type CNNs; space invariant cellular neural networks; Algorithm design and analysis; Automation; Cellular neural networks; Cost function; Genetic algorithms; Image processing; Neural networks; Programmable logic arrays; Robustness; Stability;
fLanguage
English
Journal_Title
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
Publisher
ieee
ISSN
1057-7122
Type
jour
DOI
10.1109/81.238343
Filename
238343
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