Premium
Minimizing the effects of parameter deviations on cellular neural networks
Author(s) -
Tetzlaff R.,
Kunz R.,
Wolf D.
Publication year - 1999
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/(sici)1097-007x(199901/02)27:1<77::aid-cta41>3.0.co;2-0
Subject(s) - cellular neural network , independence (probability theory) , artificial neural network , computer science , sensitivity (control systems) , algorithm , image (mathematics) , artificial intelligence , mathematics , pattern recognition (psychology) , statistics , electronic engineering , engineering
The sensitivity of cellular neural networks (CNN) against random parameter deviations is discussed in detail. For different CNN with erroneous parameters the probability is estimated that all cell outputs converge to the same stable fixpoint of the corresponding error free CNN. These results are compared with approximations based on a statistical independence assumption. The influence of deviated parameters is demonstrated for different image processing templates. We propose a new parameter learning method for minimizing the effect of template and bias deviations. In all treated cases a significant improvement can be observed by using this method. Copyright © 1999 John Wiley & Sons, Ltd.