Title :
Statistical error modeling of CNN-UM architectures: the binary case
Author_Institution :
Comput. & Autom. Res. Inst., Hungarian Acad. of Sci., Budapest, Hungary
Abstract :
In this paper a detailed error model is analyzed of the CNN-UM in a general statistical manner. The locally regular template class is considered and the possibility of erroneous output is expressed from the component nonlinearity and parameter deviation.
Keywords :
cellular neural nets; error statistics; neural chips; neural net architecture; CNN-UM architectures; binary input-output locally regular operations; component nonlinearity; erroneous output; locally regular template class; parameter deviation; statistical error modeling; Automation; Cellular neural networks; Computer aided software engineering; Computer architecture; Computer errors; Covariance matrix; Electronic mail; Gray-scale; Statistical analysis; Very large scale integration;
Conference_Titel :
Cellular Neural Networks and Their Applications, 2002. (CNNA 2002). Proceedings of the 2002 7th IEEE International Workshop on
Print_ISBN :
981-238-121-X
DOI :
10.1109/CNNA.2002.1035085
